Variational Autoencoder Papers

• Improving Sample Quality by Training and Sampling from Latent Energy
• A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
• Many-to-Many Voice Conversion using Cycle-Consistent Variational Autoencoder with Multiple Decoders
• Evidential Disambiguation of Latent Multimodality in Conditional Variational Autoencoders
• Non-parallel Voice Conversion with Controllable Speaker Individuality using Variational Autoencoder
• Cross-population Variational Autoencoders
• Improving Multimodal Generative Models with Disentangled Latent Partitions
• On variational lower bounds of mutual information
• Unsupervised Disentanglement of Pitch and Timbre for Isolated Musical Instrument Sounds
• Group-based Learning of Disentangled Representations with Generalizability for Novel Contents
• Hierarchical Linear Disentanglement of Data-Driven Conceptual Spaces
• T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story Completion
• Joint separation, dereverberation and classification of multiple sources using multichannel variational autoencoder with auxiliary classifier
• Variational Attention using Articulatory Priors for generating Code Mixed Speech using Monolingual Corpora
• Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery
• Representation Learning: A Review and New Perspectives
• Auto-Encoding Variational Bayes
• Undirected Graphical Models as Approximate Posteriors
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• NICE: Non-linear Independent Components Estimation
• Unsupervised Learning of Spatiotemporally Coherent Metrics
• DRAW: A Recurrent Neural Network For Image Generation
• Deep Learning and the Information Bottleneck Principle
• Deep Convolutional Inverse Graphics Network
• Training generative neural networks via Maximum Mean Discrepancy optimization
• Variational Inference with Normalizing Flows
• Domain-Adversarial Training of Neural Networks
• A Recurrent Latent Variable Model for Sequential Data
• Stacked What-Where Auto-encoders
• Importance Weighted Autoencoders
• The Variational Fair Autoencoder
• A note on the evaluation of generative models
• Adversarial Autoencoders
• Neural Variational Inference for Text Processing
• Attribute2Image: Conditional Image Generation from Visual Attributes
• Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization
• Autoencoding beyond pixels using a learned similarity metric
• Neighborhood-aware autoencoder for missing value imputation
• Variational Inference: A Review for Statisticians
• Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
• How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks
• Generating Images with Perceptual Similarity Metrics based on Deep Networks
• Auxiliary Deep Generative Models
• Variational methods for Conditional Multimodal Deep Learning
• Variational Autoencoder for Semi-supervised Text Classification
• Composing graphical models with neural networks for structured representations and fast inference
• Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
• Stick-Breaking Variational Autoencoders
• Discovering Causal Signals in Images
• Density estimation using Real NVP
• f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
• DISCO Nets: DISsimilarity COefficient Networks
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Improved Variational Inference with Inverse Autoregressive Flow
• Tutorial on Variational Autoencoders
• Unsupervised Learning of 3D Structure from Images
• Domain Separation Networks
• Discrete Variational Autoencoders
• Neural Photo Editing with Introspective Adversarial Networks
• Deep Feature Consistent Variational Autoencoder
• Learning What and Where to Draw
• Deep Variational Canonical Correlation Analysis
• The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
• Information Dropout: Learning Optimal Representations Through Noisy Computation
• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
• Disentangling factors of variation in deep representations using adversarial training
• PixelVAE: A Latent Variable Model for Natural Images
• Infinite Variational Autoencoder for Semi-Supervised Learning
• Improving Variational Auto-Encoders using Householder Flow
• Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
• Deep Variational Information Bottleneck
• A Survey of Inductive Biases for Factorial Representation-Learning
• Sequential Learning and Regularization in Variational Recurrent Autoencoder
• A General and Adaptive Robust Loss Function
• Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
• Reconstruction-Based Disentanglement for Pose-invariant Face Recognition
• Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
• Learning Hierarchical Features from Generative Models
• Towards a Deeper Understanding of Variational Autoencoding Models
• Opening the black box of Deep Neural Networks via Information
• Autoencoding Variational Inference For Topic Models
• Nonparametric Variational Auto-encoders for Hierarchical Representation Learning
• It Takes (Only) Two: Adversarial Generator-Encoder Networks
• Reinterpreting Importance-Weighted Autoencoders
• A Neural Representation of Sketch Drawings
• Learning Latent Representations for Speech Generation and Transformation
• VAE with a VampPrior
• VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
• Causal Effect Inference with Deep Latent-Variable Models
• Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
• Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
• Discovering Discrete Latent Topics with Neural Variational Inference
• Fader Networks: Manipulating Images by Sliding Attributes
• On Unifying Deep Generative Models
• Emergence of Invariance and Disentanglement in Deep Representations
• InfoVAE: Balancing Learning and Inference in Variational Autoencoders
• Variational Approaches for Auto-Encoding Generative Adversarial Networks
• Hidden Talents of the Variational Autoencoder
• Symmetric Variational Autoencoder and Connections to Adversarial Learning
• On the challenges of learning with inference networks on sparse, high-dimensional data
• Stochastic Variational Video Prediction
• Fixing a Broken ELBO
• Neural Discrete Representation Learning
• Wasserstein Auto-Encoders
• Challenges in Disentangling Independent Factors of Variation
• DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
• Advances in Variational Inference
• Causal Generative Neural Networks
• JADE: Joint Autoencoders for Dis-Entanglement
• Learning Independent Causal Mechanisms
• Variational Attention for Sequence-to-Sequence Models
• PixelSNAIL: An Improved Autoregressive Generative Model
• One-shot Voice Conversion with Disentangled Representations by Leveraging Phonetic Posteriorgrams
• Inference Suboptimality in Variational Autoencoders
• Mutual Information Neural Estimation
• Semi-Amortized Variational Autoencoders
• Coulomb Autoencoders
• On the Latent Space of Wasserstein Auto-Encoders
• Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs inWeb Applications
• Tighter Variational Bounds are Not Necessarily Better
• Leveraging the Exact Likelihood of Deep Latent Variable Models
• Isolating Sources of Disentanglement in VAEs
• Learning Deep Disentangled Embeddings With the F-Statistic Loss
• Variational Autoencoders for Collaborative Filtering
• Disentangling by Factorising
• Interpretable VAEs for nonlinear group factor analysis
• Distribution Matching in Variational Inference
• Sylvester Normalizing Flows for Variational Inference
• Probabilistic Video Generation using Holistic Attribute Control
• World Models
• Neural Autoregressive Flows
• Hyperspherical Variational Auto-Encoders
• Training VAEs Under Structured Residuals
• Structured Disentangled Representations
• Understanding disentangling in Beta-VAE
• Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
• Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
• Hamiltonian Variational Auto-Encoder
• Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
• Temporal Difference Variational Auto-Encoder
• Manifold Mixup: Better Representations by Interpolating Hidden States
• Autoregressive Quantile Networks for Generative Modeling
• The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models
• InfoCatVAE: Representation Learning with Categorical Variational Autoencoders
• Pioneer Networks: Progressively Growing Generative Autoencoder
• Glow: Generative Flow with Invertible 1×1 Convolutions
• Handling Incomplete Heterogeneous Data using VAEs
• Representation Learning with Contrastive Predictive Coding
• TherML: Thermodynamics of Machine Learning
• Explorations in Homeomorphic Variational Auto-Encoding
• Avoiding Latent Variable Collapse with Generative Skip Models
• IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
• Iterative Amortized Inference
• Variational Memory Encoder-Decoder
• Diverse Image-to-Image Translation via Disentangled Representations
• Unbiased Implicit Variational Inference
• Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects
• ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder
• Learning deep representations by mutual information estimation and maximization
• Interpretable Intuitive Physics Model
• Spherical Latent Spaces for Stable Variational Autoencoders
• Taming VAEs
• The Dreaming Variational Autoencoder for Reinforcement Learning Environments
• FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
• Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
• Do Deep Generative Models Know What They Don't Know?
• Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
• VIREL: A Variational Inference Framework for Reinforcement Learning
• Bias and Generalization in Deep Generative Models: An Empirical Study
• VV-NET: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation
• A General Method for Amortizing Variational Filtering
• Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
• Towards a Definition of Disentangled Representations
• Disentangling Disentanglement in Variational Autoencoders
• Counterfactuals uncover the modular structure of deep generative models
• Recent Advances in Autoencoder-Based Representation Learning
• Learning Latent Subspaces in Variational Autoencoders
• Variational Autoencoders Pursue PCA Directions (by Accident)
• Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions
• MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
• Preventing Posterior Collapse with delta-VAEs
• Practical Lossless Compression with Latent Variables using Bits Back Coding
• Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
• Disentangling Video with Independent Prediction
• Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
• Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
• What does the free energy principle tell us about the brain?
• Learning Disentangled Representations with Reference-Based Variational Autoencoders
• MONet: Unsupervised Scene Decomposition and Representation
• Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
• BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
• Contrastive Variational Autoencoder Enhances Salient Features
• Disentangled Representation Learning for 3D Face Shape
• Multi-Object Representation Learning with Iterative Variational Inference
• PuVAE: A Variational Autoencoder to Purify Adversarial Examples
• Variational Auto-Decoder
• Diagnosing and Enhancing VAE Models
• Unsupervised Part-Based Disentangling of Object Shape and Appearance
• M2VAE – Derivation of a Multi-Modal Variational Autoencoder Objective from the Marginal Joint Log-Likelihood
• AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformations
• Wasserstein Dependency Measure for Representation Learning
• From Variational to Deterministic Autoencoders
• Variational Adversarial Active Learning
• Generalized Variational Inference: Three arguments for deriving new Posteriors
• Riemannian Normalizing Flow on VariationalWasserstein Autoencoder for Text Modeling
• Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection
• A Variational Auto-Encoder Model for Stochastic Point Processes
• Gait Recognition via Disentangled Representation Learning
• LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
• A Deep Generative Model for Graph Layout
• Variational Autoencoders for Sparse and Overdispersed Discrete Data
• Disentangling Factors of Variations Using Few Labels
• Data-Efficient Mutual Information Neural Estimator
• Affine Variational Autoencoders: An Efficient Approach for Improving Generalization and Robustness to Distribution Shift
• DIVA: Domain Invariant Variational Autoencoders
• Are Disentangled Representations Helpful for Abstract Visual Reasoning?
• Unsupervised Model Selection for Variational Disentangled Representation Learning
• AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
• On the Fairness of Disentangled Representations
• Generating Diverse High-Fidelity Images with VQ-VAE-2
• Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
• Weakly Supervised Disentanglement by Pairwise Similarities
• An Introduction to Variational Autoencoders
• Disentangled State Space Representations
• On the Transfer of Inductive Bias from Simulation to the RealWorld: a New Disentanglement Dataset
• Reweighted Expectation Maximization
• Disentangled Inference for GANs with Latently Invertible Autoencoder
• Derivation of the Variational Bayes Equations
• Demystifying Inter-Class Disentanglement
• Guided Image Generation with Conditional Invertible Neural Networks
• GP-VAE: Deep Probabilistic Time Series Imputation
• Variational Autoencoders and Nonlinear ICA: A Unifying Framework
• Predicting Visual Memory Schemas with Variational Autoencoders
• Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function
• LayoutVAE: Stochastic Scene Layout Generation From a Label Set
• Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
• Likelihood Contribution based Multi-scale Architecture for Generative Flows
• Video Compression With Rate-Distortion Autoencoders
• Geometric Disentanglement for Generative Latent Shape Models
• Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
• Spectral Regularization for Combating Mode Collapse in GANs
• Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement
• Neural Gaussian Copula for Variational Autoencoder
• Real Time Trajectory Prediction Using Deep Conditional Generative Models
• Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation
• Variable Rate Deep Image CompressionWith a Conditional Autoencoder
• Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
• Disentangling Speech and Non-Speech Components for Building Robust Acoustic Models from Found Data
• LAVAE: Disentangling Location and Appearance
• Implicit Discriminator in Variational Autoencoder
• Evaluating Disentangled Representations
• Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement
• Weakly Supervised Disentanglement with Guarantees
• Leveraging directed causal discovery to detect latent common causes
• Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis
• Exploiting video sequences for unsupervised disentangling in generative adversarial networks
• Bridging the ELBO and MMD
• Neural Density Estimation and Likelihood-free Inference
• Learning Disentangled Representations for Recommendation
• Continual Unsupervised Representation Learning
• High-dimensional Nonlinear Profile Monitoring based on Deep Probabilistic Autoencoders
• Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement
• Mixed-curvature Variational Autoencoders
• dpVAEs: Fixing Sample Generation for Regularized VAEs
• Contrastive Learning of Structured World Models
• Representing Closed Transformation Paths in Encoded Network Latent Space
• Gaussian Process Priors for View-Aware Inference
• Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes
• MaskAAE: Latent space optimization for Adversarial Auto-Encoders
• Multimodal Generative Models for Compositional Representation Learning
• A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction
• SGVAE: Sequential Graph Variational Autoencoder
• The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
• RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
• Learning Predictive Models From Observation and Interaction
• Disentangled Representation Learning with Wasserstein Total Correlation
• Learning Representations by Maximizing Mutual Information in Variational Autoencoders
• Phase Transitions for the Information Bottleneck in Representation Learning
• AE-OT-GAN: Training GANs from data specific latent distribution
• High-Fidelity Synthesis with Disentangled Representation
• Adversarial Disentanglement with Grouped Observations
• Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
• Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion
• Controlling generative models with continuous factors of variations
• Learning Discrete Distributions by Dequantization
• CosmoVAE: Variational Autoencoder for CMB Image Inpainting
• On Implicit Regularization in beta-VAEs
• Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder
• Unbalanced GANs: Pre-training the Generator of Generative Adversarial Network using Variational Autoencoder
• Weakly-Supervised Disentanglement Without Compromises
• Learning Flat Latent Manifolds with VAEs
• MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic
• Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoders
• Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learning
• Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
• Variance Loss in Variational Autoencoders
• q-VAE for Disentangled Representation Learning and Latent Dynamical Systems
• A Robust Speaker Clustering Method Based on Discrete Tied Variational Autoencoder
• mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder
• Multi-Objective Variational Autoencoder: an Application for Smart Infrastructure Maintenance
• Stochastic Virtual Battery Modeling of Uncertain Electrical Loads using Variational Autoencoder
• Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of beta-VAE
• Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression
• Dynamic Narrowing of VAE Bottlenecks Using GECO and L0 Regularization
• VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
• A lower bound for the ELBO of the Bernoulli Variational Autoencoder
• Epitomic Variational Graph Autoencoder
• Towards democratizing music production with AI—Design of Variational Autoencoder-based Rhythm Generator as a DAW plugin
• AI Giving Back to Statistics? Discovery of the Coordinate System of Univariate Distributions by Beta Variational Autoencoder
• PatchVAE: Learning Local Latent Codes for Recognition
• Normalizing Flows with Multi-Scale Autoregressive Priors
• Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
• OPTIMUS: Organizing Sentences via Pre-trained Modeling of a Latent Space
• Adversarial Latent Autoencoders
• Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders
• ControlVAE: Controllable Variational Autoencoder
• Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder
• CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models
• On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond
• Discretized Bottleneck: Posterior-Collapse-Free Sequence-to-Sequence Learning
• Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation
• Conditioned Variational Autoencoder for top-N item recommendation
• A Batch Normalized Inference Network Keeps the KL Vanishing Away
• Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder
• Multi-Decoder RNN Autoencoder Based on Variational Bayes Method
• beta-Variational Autoencoder as an Entanglement Classifier
• Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
• Preventing Posterior Collapse with Levenshtein Variational Autoencoder
• Mutual Information Gradient Estimation for Representation Learning
• Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection
• Variance Constrained Autoencoding
• Prototypical Contrastive Learning of Unsupervised Representations
• Deep Latent Variable Model for Learning Longitudinal Multi-view Data
• Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders
• Semi-supervised Neural Chord Estimation Based on a Variational Autoencoder with Latent Chord Labels and Features
• A Deeper Look at the Unsupervised Learning of Disentangled Representations in β-VAE from the Perspective of Core Object Recognition
• Face Identity Disentanglement via Latent Space Mapping
• HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network
• Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
• S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
• Deep Learning on the 2-Dimensional Ising Model to Extract the Crossover Region with a Variational Autoencoder
• Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution
• VMI-VAE: Variational Mutual Information Maximization Framework for VAE With Discrete and Continuous Priors
• High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder
• NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces
• Variational Mutual Information Maximization Framework for VAE Latent Codes with Continuous and Discrete Priors
• Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems
• MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning
• Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system
• tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder
• Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization
• AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
• VAEs in the Presence of Missing Data
• Interpretable Deep Graph Generation with Node-edge Co-disentanglement
• Probabilistic Auto-Encoder
• To Regularize or Not To Regularize? The Bias Variance Trade-off in Regularized AEs
• DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors
• An Improved Semi-Supervised VAE for Learning Disentangled Representations
• High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
• Structural Autoencoders Improve Representations for Generation and Transfer
• Disentanglement for Discriminative Visual Recognition
• Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy
• Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder
• Robust Variational Autoencoder for Tabular Data with beta Divergence
• Learning Latent Space Energy-Based Prior Model
• Learning from Demonstration with Weakly Supervised Disentanglement
• Isometric Autoencoders
• Density Deconvolution with Normalizing Flows
• Longitudinal Variational Autoencoder
• Towards Recurrent Autoregressive Flow Models
• Rethinking Semi–Supervised Learning in VAEs
• A Tutorial on VAEs: From Bayes' Rule to Lossless Compression
• Variational Autoencoder with Learned Latent Structure
• Constraining Variational Inference with Geometric Jensen-Shannon Divergence
• DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
• Latent Variable Modeling with Random Features
• Denoising Diffusion Probabilistic Models
• Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
• VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
• Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
• Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
• Disentangling by Subspace Diffusion
• Variational Orthogonal Features
• Mixture of Discrete Normalizing Flows for Variational Inference
• SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
• Efficient Learning of Generative Models via Finite-Difference Score Matching
• Benefiting Deep Latent Variable Models via Learning the Prior and Removing Latent Regularization
• Contrastive Code Representation Learning
• Self-Reflective Variational Autoencoder
• Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model
• Reconstruction Bottlenecks in Object-Centric Generative Models
• Deep Heterogeneous Autoencoder for Subspace Clustering of Sequential Data
• Relaxed-Responsibility Hierarchical Discrete VAEs
• Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
• Learning Invariances for Interpretability using Supervised VAE
• Generative Flows with Matrix Exponential
• Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
• Transferred Discrepancy: Quantifying the Difference Between Representations
• Learning Disentangled Representations with Latent Variation Predictability
• A Commentary on the Unsupervised Learning of Disentangled Representations
• Approximation Based Variance Reduction for Reparameterization Gradients
• Privacy-preserving Voice Analysis via Disentangled Representations
• dMelodies: A Music Dataset for Disentanglement Learning
• Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
• Rewriting a Deep Generative Model
• Geometrically Enriched Latent Spaces
• Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning
• SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
• LoCo: Local Contrastive Representation Learning
• PDE-Driven Spatiotemporal Disentanglement
• Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach
• What Should Not Be Contrastive in Contrastive Learning
• Learning Interpretable Representation for Controllable Polyphonic Music Generation
• Disentangled Item Representation for Recommender Systems
• Joint Variational Autoencoders for Recommendation with Implicit Feedback
• Linear Disentangled Representations and Unsupervised Action Estimation
• Variational Autoencoder for Anti-Cancer Drug Response Prediction
• Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder
• Metrics for Exposing the Biases of Content-Style Disentanglement
• Dynamical Variational Autoencoders: A Comprehensive Review
• Quasi-symplectic Langevin Variational Autoencoder
• Ordinal-Content VAE: Isolating Ordinal-Valued Content Factors in Deep Latent Variable Models
• Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
• DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning
• Deep Transparent Prediction through Latent Representation Analysis
• Decoupling Representation Learning from Reinforcement Learning
• Improving Robustness and Generality of NLP Models Using Disentangled Representations
• Hierarchical Sparse Variational Autoencoder for Text Encoding
• Embedding and generation of indoor climbing routes with variational autoencoder
• Targeted VAE: Structured Inference and Targeted Learning for Causal Parameter Estimation
• Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
• RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
• LEGAN: Disentangled Manipulation of Directional Lighting and Facial Expressions by Leveraging Human Perceptual Judgements
• Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains
• Unsupervised Hierarchical Concept Learning
• Disentangled Generative Causal Representation Learning
• NCP-VAE: Variational Autoencoders with Noise Contrastive Priors
• Learning disentangled representations with the Wasserstein Autoencoder
• Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders
• Dirichlet Graph Variational Autoencoder
• Baseline System of Voice Conversion Challenge 2020 with Cyclic Variational Autoencoder and ParallelWaveGAN
• Discrete Latent Space World Models for Reinforcement Learning
• A variational autoencoder for music generation controlled by tonal tension
• The NeteaseGames System for Voice Conversion Challenge 2020 with Vector-quantization Variational Autoencoder and WaveNet
• On the surprising similarities between supervised and self-supervised models
• Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling
• Learning Optimal Conditional Priors For Disentangled Representations
• Sparse Gaussian Process Variational Autoencoders
• Geometry-Aware Hamiltonian Variational Auto-Encoder
• Quaternion-Valued Variational Autoencoder
• Disentangling Action Sequences: Finding Correlated Images
• Scalable Gaussian Process Variational Autoencoders
• Wavelet Flow: Fast Training of High Resolution Normalizing Flows
• Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference
• The Evidence Lower Bound of Variational Autoencoders Converges to a Sum of Three Entropies
• Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation
• MAD-VAE: Manifold Awareness Defense Variational Autoencoder
• Quantifying and Learning Disentangled Representations with Limited Supervision
• VCE: Variational Convertor-Encoder for One-Shot Generalization
• On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision
• Factorized Gaussian Process Variational Autoencoders
• Speech Prediction in Silent Videos using Variational Autoencoders
• Mutual Information based Method for Unsupervised Disentanglement of Video Representation
• Vector Embeddings with Subvector Permutation Invariance using a Triplet Enhanced Autoencoder
• Use of Student's t -Distribution for the Latent Layer in a Coupled Variational Autoencoder
• Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
• Integration of variational autoencoder and spatial clustering for adaptive multi-channel neural speech separation
• Adaptive Efficient Coding: A Variational Auto-encoder Approach
• Predictive Auxiliary Variational Autoencoder for Representation Learning of Global Speech Characteristics
• Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
• The information bottleneck method
• InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
• Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
• Distribution Augmentation for Generative Modeling
• Variational Bayesian Quantization
• Variational inference II
• Vibration Signal Generation Using Conditional Variational Autoencoder for Class Imbalance Problem
• Application of variational autoencoders for aircraft turbomachinery design
• SCAN: Learning to Classify Images without Labels
• SG-VAE: Scene Grammar Variational Autoencoder to generate new indoor scenes
• Fine-tuning Generative Models
• A Framework for the Quantitative Evaluation of Disentangled Representations
• Ae-OT: a New Generative Model based on Extended Semi-discrete Optimal transport
• Continuous Representation of Molecules Using Graph Variational Autoencoder
• Understanding Degeneracies and Ambiguities in Attribute Transfer
• Auxiliary Guided Autoregressive Variational Autoencoders
• BetaVAE: Learning basic visual concepts with a constrained variational framework
• Robust Discrimination and Generation of Faces using Compact, Disentangled Embeddings
• Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation
• Semi-supervised Deep Learning in Motor Imagery-Based Brain-Computer Interfaces with Stacked Variational Autoencoder
• PuppeteerGAN: Arbitrary Portrait Animation with Semantic-aware Appearance Transformation
• Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation
• Couple-VAE: Mitigating the Encoder-Decoder Incompatibility in Variational Text Modeling with Coupled Deterministic Networks
• CRUDS: Counterfactual Recourse Using Disentangled Subspaces
• Factorized Variational Autoencoders for Modeling Audience Reactions to Movies
• Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction
• Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference
• Deep variational autoencoders for breast cancer tissue modeling and synthesis in SFDI
• Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
• Disentanglement with Hyperspherical Latent Spaces using Diffusion Variational Autoencoders
• Do Compressed Representations Generalize Better?
• SRFlow: Learning the Super-Resolution Space with Normalizing Flow
• The Mutual Autoencoder: Controlling Information in Latent Code Representations
• Generalization via Information Bottleneck in Deep Reinforcement Learning
• EEG-Based Adaptive Driver-Vehicle Interface Using Variational Autoencoder and PI-TSVM
• Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
• A Two-Step Disentanglement Method
• Rare Event Detection using Disentangled Representation Learning
• Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
• ELBO surgery: yet another way to carve up the variational evidence lower bound
• Learning Sampling in Financial Statement Audits using Vector Quantised Variational Autoencoder Neural Networks
• Identity from here, Pose from there: Self-supervised Disentanglement and Generation of Objects using Unlabeled Videos
• Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
• Variational Learning of Bayesian Neural Networks via Bayesian Dark Knowledge
• Improved Disentanglement through Aggregated Convolutional Feature Maps
• Variational Learning and Bits-Back Coding: An Information-Theoretic View to Bayesian Learning
• ISA-VAE: Independent Subspace Analysis with Variational Autoencoders
• Isolating Latent Structure with Cross-population Variational Autoencoders
• Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
• Joint Multimodal Learning with Deep Generative Models
• Sparsity enforcement on latent variables for better disentanglement in VAE
• Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders
• OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder
• Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
• On casting importance weighted autoencoder to an EM algorithm to learn deep generative models
• Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
• Variational Inference for Sequential Data with Future Likelihood Estimates
• Learning Deep Latent-variable MRFs with Amortized Bethe Free Energy Minimization
• Learning Disentangled Representations with Wasserstein Auto-Encoders
• Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
• Learning Robust Representations by Projecting Superficial Statistics Out
• Wild Variational Approximations
• Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection
• Causal Discovery with Attention-Based Convolutional Neural Networks
• Neural Decomposition: Functional ANOVA with Variational Autoencoders
• Max-Affine Spline Insights into Deep Generative Networks
• Mixtures of Variational Autoencoders
• Translating Visual Art into Music
• Dual Swap Disentangling
• Gaussian Process Prior Variational Autoencoders
• Information Constraints on Auto-Encoding Variational Bayes
• Invariant Representations without Adversarial Training
• Learning Disentangled Joint Continuous and Discrete Representations
• Multimodal Generative Models for Scalable Weakly-Supervised Learning
• Adaptive Density Estimation for Generative Models
• A Latent Variational Framework for Stochastic Optimization
• A Primal-Dual link between GANs and Autoencoders
• Conditional Structure Generation through Graph Variational Generative Adversarial Nets
• Direct Optimization through arg max for Discrete Variational Auto-Encoder
• Discrete Flows: Invertible Generative Models of Discrete Data
• Disentangled behavioral representations
• Disentangling Influence: Using disentangled representations to audit model predictions
• Exact Rate-Distortion in Autoencoders via Echo Noise
• Explicit Disentanglement of Appearance and Perspective in Generative Models
• Explicitly disentangling image content from translation and rotation with spatial-VAE
• Implicit Posterior Variational Inference for Deep Gaussian Processes
• Invertible Convolutional Flow
• Learning Disentangled Representation for Robust Person Re-identification
• Learning Hierarchical Priors in VAEs
• Learning-In-The-Loop Optimization: End-To-End Control And Co-Design of Soft Robots Through Learned Deep Latent Representations
• MaCow: Masked Convolutional Generative Flow
• MAVEN: Multi-Agent Variational Exploration
• ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
• Provable Gradient Variance Guarantees for Black-Box Variational Inference
• Re-examination of the Role of Latent Variables in Sequence Modeling
• Residual Flows for Invertible Generative Modeling
• Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
• Semi-Implicit Graph Variational Auto-Encoders
• Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
• The continuous Bernoulli: fixing a pervasive error in variational autoencoders
• The Thermodynamic Variational Objective
• Triad Constraints for Learning Causal Structure of Latent Variables
• Variational Graph Recurrent Neural Networks
• Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
• Variational Temporal Abstraction
• CompRess: Self-Supervised Learning by Compressing Representations
• Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
• Gradient Boosted Normalizing Flows
• Learning Disentangled Representations and Group Structure of Dynamical Environments
• Learning Disentangled Representations of Video with Missing Data
• The Autoencoding Variational Autoencoder
• Semi-supervised Learning with Deep Generative Models
• Learning Structured Output Representation using Deep Conditional Generative Models
• Variational Autoencoder for Deep Learning of Images, Labels and Captions
• Learning Disentangled Representations with Semi-Supervised Deep Generative Models
• Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
• Unsupervised Learning of Disentangled Representations from Video
• Task-Conditioned Variational Autoencoders for Learning Movement Primitives
• Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision
• Content and Style Disentanglement for Artistic Style Transfer
• Variational Laplace Autoencoders
• No Representation without Transformation
• Interpretable Models in Probabilistic Machine Learning
• Unsupervised Clustering through Gaussian Mixture Variational AutoEncoder with Non-Reparameterized Variational Inference and Std Annealing
• VAE-BRIDGE: Variational Autoencoder Filter for Bayesian Ridge Imputation of Missing Data
• Topologically-based Variational Autoencoder for Time Series Classification
• Predictive Coding, Variational Autoencoders, and Biological Connections
• PVAE: Learning Disentangled Representations with Intrinsic Dimension via Approximated L0 Regularization
• Unsupervised Representation Learning in Interactive Environments
• Learning to Disentangle Factors of Variation with Manifold Interaction
• Semi-supervised Learning With Temporal Variational Auto-encoders For The Diagnosis Of Failure Severities And The Prognosis Of Remaining Useful Life
• Deep Generative Video Compression with Temporal Autoregressive Transforms
• Deep Latent-Variable Models for Natural Language Understanding and Generation
• Neural Face Editing with Intrinsic Image Disentangling
• One-Class Collaborative Filtering with the Queryable Variational Autoencoder
• Deep Critiquing for VAE-based Recommender Systems
• Sliced Wasserstein Auto-Encoders
• Multi-Task with Variational Autoencoder for Lung Cancer Prognosis on Clinical Data
• Faster Attend-Infer-Repeat with Tractable Probabilistic Models
• The Mutual Autoencoder: Controlling Information in Latent Code Representations
• Semi-supervised Self-produced Speech Enhancement and Suppression Based on Joint Source Modeling of Air- and Body-conducted Signals Using Variational Autoencoder
• Hierarchical Disentanglement of Discriminative Latent Features for Zero-shot Learning
• Transforming Auto-encoders
• Understanding Posterior Collapse in Generative Latent Variable Models
• Unsupervised Discovery of Interpretable Latent Manipulations in Language VAEs
• A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks
• Variational Autoencoder and Extensions
• DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
• Variational Autoencoders with Jointly Optimized Latent Dependency Structure
• Variational Learning with Disentanglement-PyTorch
• Variational online learning of neural dynamics
• Non-parallel Voice Conversion based on Hierarchical Latent Embedding Vector Quantized Variational Autoencoder
• A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
• A Semi-supervised Deep Generative Model for Human Body Analysis
• Comment: Variational Autoencoders as Empirical Bayes
• Complex-Valued Variational Autoencoder: A Novel Deep Generative Model for Direct Representation of Complex Spectra
• Adaptive Neural Speech Enhancement with a Denoising Variational Autoencoder
• Self-supervised learning of a facial attribute embedding from video
• Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items
• TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation
• Learning Latent Representations to Influence Multi-Agent Interaction
• Bridged Variational Autoencoders for Joint Modeling of Images and Attributes
• Disentangled Representations for Sequence Data using Information Bottleneck Principle
• Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding
• Disentangling Latent Hands for Image Synthesis and Pose Estimation
• Variational Few-Shot Learning