Machine Learning paper reviews for personal archiving purpose.
Refer to [Issues] tab. (issues without labels are TBD)
| No | Title | Venue | Year | Link |
|---|---|---|---|---|
| 1 | Learning representation from backpropagating errors | 1986 | [pdf] | |
| 2 | Unpaired Image to Image Translation using cycle-consistent advarsarial net | ICCV | 2017 | [pdf] |
| 3 | Generative Adversarial Nets | NIPS | 2014 | [pdf] |
| 4 | Understanding Deep Learning requires rethinking Generalization | CVPR | 2017 | [pdf] |
| 5 | Show and Tell - A neural Image Caption Generator | CVPR | 2015 | [pdf] |
| 6 | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks | ICLR | 2016 | [pdf] |
| 7 | Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models | 2017 | [pdf] | |
| 8 | Conditional Generative Adversarial Nets | 2014 | [pdf] | |
| 9 | CartoonGAN: Generative Adversarial Networks for Photo Cartoonization | CVPR | 2018 | [pdf] |
| 10 | Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding | KDD | 2019 | [pdf] |
| 11 | Jukebox: A Generative Model for Music | 2020 | [pdf] | |
| 12 | Revisiting Self-supervised Visual Representation Learning | CVPR | 2019 | [pdf] |
| 13 | Reposing Humans by Warping 3D Features | CVPRW | 2020 | [pdf] |
| 14 | Improving Language Understanding by Generative Pre-Training | 2018 | [pdf] | |
| 15 | Language Models are Unsupervised Multitask Learners | 2019 | [pdf] | |
| 16 | Implicit Maximum Likelihood Estimation | 2018 | [pdf] | |
| 17 | Pose Guided Person Image Generation | NIPS | 2017 | [pdf] |
| 18 | Progressive Growing of GANs for Improved Quality, Stability and Variation | ICLR | 2018 | [pdf] |
| 19 | Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization | ICCV | 2017 | [pdf] |
| 20 | Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN | ACPR | 2017 | [pdf] |
| 21 | Continuous Control with Deep Reinforcement Learning | 2015 | [pdf] | |
| 22 | Every Model Learned by Gradient Descent is Apporximately a Kernel Machine | 2020 | [pdf] | |
| 23 | Closed-Form Factorization of Latent Semantics in GANs | CVPR | 2021 | [pdf] |
| 24 | Large Scale GAN Training for High Fidelity Natural Image Synthesis | ICLR | 2019 | [pdf] |
| 25 | Im2Pencil: Controllable Pencil Illustration from Photographs | CVPR | 2019 | [pdf] |
| 26 | End-to-End Time-Lapse Video Synthesis from a Single Outdoor Image | CVPR | 2019 | [pdf] |
| 27 | Improved Precision and Recall Metric for Assessing Generative Models | NIPS | 2019 | [pdf] |
| 28 | Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs | CVPRW | 2020 | [pdf] |
| 29 | Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | CVPR | 2017 | [pdf] |
| 30 | Sharpness-Aware Minimization for Efficiently Improving Generalization | ICLR | 2020 | [pdf] |
| 31 | Differentiable Augmentation for Data-Efficient GAN Training | NIPS | 2020 | [pdf] |
| 32 | ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness | ICLR | 2019 | [pdf] |
| 33 | U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation | ICLR | 2020 | [pdf] |
| 34 | In-Domain GAN Inversion for Real Image Editing | ECCV | 2020 | [pdf] |
| 35 | Feature Quantization Improves GAN Training | ICML | 2020 | [pdf] |
| 36 | Improved Techniques for Training GANs | NIPS | 2016 | [pdf] |
| 37 | Generating Diverse High-Fidelity Images with VQ-VAE-2 | NIPS | 2019 | [pdf] |
| 38 | Wide-Context Semantic Image Extrapolation | CVPR | 2019 | [pdf] |
| 39 | Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs | ICLR | 2021 | [pdf] |
| 40 | Swapping Autoencoder for Deep Image Manipulation | NIPS | 2020 | [pdf] |
| 41 | Training GANs with Stronger Augmentations via Contrastive Discriminator | ICLR | 2021 | [pdf] |
| 42 | Using Latent Space Regression to Analyze and Leverage Compositionality in GANs | 2021 | [pdf] | |
| 43 | On Self-supervised Image Representations For GAN Evaluation | ICLR | 2021 | [pdf] |
| 44 | A Good Image Generator is What You Need for High-Resolution Video Synthesis | ICLR | 2021 | [pdf] |
| 45 | Adversarial Latent Autoencoders | 2020 | [pdf] | |
| 46 | The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks | ICLR | 2019 | [pdf] |
| 47 | How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks | ICLR | 2021 | [pdf] |
| 48 | Density Estimation Using Real NVP | ICLR | 2017 | [pdf] |
| 49 | Unsupervised Learning of Probably Symmetric Deformable 3D Object from Images in the Wild | CVPR | 2020 | [pdf] |
| 50 | NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis | CVPR | 2020 | [pdf] |
| 51 | Dataset Condensation with Gradient Matching | ICLR | 2021 | [pdf] |
| 52 | Momentum Contrast for Unsupervised Visual Representation Learning | CVPR | 2020 | [pdf] |
| 53 | Interpreting the Latent Space of GANs for Semantic Face Editing | CVPR | 2020 | [pdf] |
| 54 | Learning Continuous Image Representation with Local Implicit Image Function | CVPR | 2021 | [pdf] |
| 55 | Bootstrap your own latent: A new approach to self-supervised Learning | NIPS | 2020 | [pdf] |
| 56 | PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models | CVPR | 2020 | [pdf] |
| 57 | Denoising Diffusion Probabilistic Models | NIPS | 2020 | [pdf] |
| 58 | Analyzing and Improving the Image Quality of StyleGAN | CVPR | 2020 | [pdf] |
| 59 | Are Convolutional Neural Networks or Transformers more like human vision? | 2021 | [pdf] | |
| 60 | Pay Attention to MLPs | 2021 | [pdf] | |
| 61 | Rethinking and Improving the Robustness of Image Style Transfer | CVPR | 2021 | [pdf] |
| 62 | Style-Aware Normalized Loss for Improving Arbitrary Style Transfer | CVPR | 2021 | [pdf] |
| 63 | GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields | CVPR | 2021 | [pdf] |
| 64 | Densely connected multidilated convolutional networks for dense prediction tasks | CVPR | 2021 | [pdf] |
| 65 | ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows | CVPR | 2021 | [pdf] |
| 66 | Dual Contradistinctive Generative Autoencoder | CVPR | 2021 | [pdf] |
| 67 | Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning | CVPR | 2021 | [pdf] |
| 68 | AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries | CVPR | 2021 | [pdf] |
| 69 | Training Networks in Null Space of Feature Covariance for Continual Learning | CVPR | 2021 | [pdf] |
| 70 | NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections | CVPR | 2021 | [pdf] |
| 71 | Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains | NIPS | 2020 | [pdf] |
| 72 | What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? | NIPS | 2017 | [pdf] |
| 73 | GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis | NIPS | 2020 | [pdf] |
| 74 | Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations | NIPS | 2019 | [pdf] |
| 75 | Neural Volumes: Learning Dynamic Renderable Volumes from Images | SIGGRAPH | 2019 | [pdf] |
| 76 | pixelNeRF: Neural Radiance Fields from One or Few Images | CVPR | 2021 | [pdf] |
| 77 | PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization | ICCV | 2019 | [pdf] |
| 78 | pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis | CVPR | 2021 | [pdf] |
| 79 | Edge Guided Progressively Generative Image Outpainting | CVPRW | 2021 | [pdf] |
| 80 | 3D Shape Generation with Grid-based Implicit Functions | CVPR | 2021 | [pdf] |
| 81 | Deep Image Prior | CVPR | 2018 | [pdf] |
| 82 | Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs | ICLR | 2021 | [pdf] |
| 83 | Taming Transformers for High-Resolution Image Synthesis | CVPR | 2021 | [pdf] |
| 84 | Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes | CVPR | 2021 | [pdf] |
| 85 | Neural Sparse Voxel Fields | NIPS | 2020 | [pdf] |
| 86 | Triplet is All You Need with Random Mappings for Unsupervised Visual Representation Learning | 2021 | [pdf] | |
| 87 | Few-shot Image Generation via Cross-domain Correspondence | CVPR | 2021 | [pdf] |
| 88 | Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space? | ICCV | 2019 | [pdf] |
| 89 | NeX: Real-time View Synthesis with neural Basis Expansion | CVPR | 2021 | [pdf] |
| 90 | Few-shot Image Generation with Elastic Weight Consolidation | NIPS | 2020 | [pdf] |
| 91 | Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis | CVPR | 2020 | [pdf] |
| 92 | On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes | ICML | 2021 | [pdf] |
| 93 | AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation | CVPR | 2020 | [pdf] |
| 94 | Softmax Splatting for Video Frame Interpolation | CVPR | 2020 | [pdf] |
| 95 | Time Lens: Event-based Video Frame Interpolation | CVPR | 2021 | [pdf] |
| 96 | Revisiting Adaptive Convolutions for Video Frame Interpolation | WACV | 2021 | [pdf] |
| 97 | Depth-Aware Video Frame Interpolation | CVPR | 2019 | [pdf] |
| 98 | Channel Attention is All You Need for Video Frame Interpolation | AAAI | 2020 | [pdf] |
| 99 | PWC-Net: CNNs for Optical Flow Using Pyramid,Warping, and Cost Volume | CVPR | 2018 | [pdf] |
| 100 | RAFT: Recurrent All-Pairs Field Transforms for Optical Flow | ECCV | 2020 | [pdf] |
| 101 | What Matters in Unsupervised Optical Flow | ECCV | 2020 | [pdf] |
| 102 | Occlusion Aware Unsupervised Learning of Optical Flow | CVPR | 2018 | [pdf] |
| 103 | UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss | AAAI | 2018 | [pdf] |
| 104 | DDFlow: Learning Optical Flow with Unlabeled Data Distillation | AAAI | 2019 | [pdf] |
| 105 | SelFlow: Self-Supervised Learning of Optical Flow | CVPR | 2019 | [pdf] |
| 106 | GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose | CVPR | 2018 | [pdf] |
| 107 | Zero Shot Text to Image Generation | OpenAI | 2021 | [pdf] |
| 108 | LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity | CVPR | 2021 | [pdf] |
| 109 | StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks | ICCV | 2017 | [pdf] |
| 110 | AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks | CVPR | 2018 | [pdf] |
| 111 | Image Synthesis From Reconfigurable Layout and Style | ICCV | 2019 | [pdf] |
| 112 | Image Generation from Layout | CVPR | 2019 | [pdf] |
| 113 | Context-aware Layout to Image Generation with Enhanced Object Appearance | CVPR | 2021 | [pdf] |
| 114 | Attribute-Guided Image Generation From Layout | BMVC | 2020 | [pdf] |
| 115 | Object-Centric Image Generation from Layouts | AAAI | 2021 | [pdf] |
| 116 | Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis | TPAMI | 2021 | [pdf] |
| 117 | BachGAN: High-Resolution Image Synthesis from Salient Object Layout | CVPR | 2020 | [pdf] |
| 118 | Region-aware Adaptive Instance Normalization for Image Harmonization | CVPR | 2021 | [pdf] |
| 119 | StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis | CVPR | 2021 | [pdf] |
| 120 | Optimizing the Latent Space of Generative Networks | ICML | 2018 | [pdf] |
| 121 | StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation | CVPR | 2021 | [pdf] |
| 122 | Autoencoder Image Interpolation by Shaping the Latent Space | ICML | 2021 | [pdf] |
| 123 | Autoencoding Under Normalization Constraints | ICML | 2021 | [pdf] |
| 124 | Cross-Modal Contrastive Contrastive Learning for Text-to-Image Generation | CVPR | 2021 | [pdf] |
| 125 | Action-Conditioned 3D Human Motion Synthesis with Transformer VAE | ICCV | 2021 | [pdf] |
| 126 | Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector | ECCV | 2020 | [pdf] |
| 127 | A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder | ICCV | 2021 | [pdf] |
| 128 | NUWA: Visual Synthesis Pre-training for Neural visUal World creAtion | - | - | [pdf] |
| 129 | Pix2seq: A Language Modeling Framework for Object Detection | - | - | [pdf] |
| 130 | Masked Autoencoders Are Scalable Vision Learners | - | 2021 | [pdf] |
| 131 | Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision | EMNLP | 2020 | [pdf] |
| 132 | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks | NIPS | 2020 | [pdf] |
| 133 | VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer | NIPS | 2021 | [pdf] |
| 134 | You Only Learn One Representation: Unified Network for Multiple Tasks | - | - | [pdf] |
| 135 | StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis | ICLR | 2022 | [pdf] |
| 136 | Co2L: Contrastive Continual Learning | ICCV | 2021 | [pdf] |
| 137 | Adversarial Generation of Continuous Images | CVPR | 2021 | [pdf] |
| 138 | MetaFormer is Actually What You Need for Vision | - | - | [pdf] |
| 139 | Rehearsal revealed: The limits and merits of revisiting samples in continual learning | ICCV | 2021 | [pdf] |
| 140 | GDumb: A Simple Approach that Questions Our Progress in Continual Learning | ECCV | 2020 | [pdf] |
| 141 | GAN Memory with No Forgetting | NIPS | 2020 | [pdf] |
| 142 | Few-Shot and Continual Learning With Attentive Independent Mechanisms | ICCV | 2021 | [pdf] |
| 143 | Self-Supervised GANs with Label Augmentation | NIPS | 2021 | [pdf] |
| 144 | Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima | NIPS | 2021 | [pdf] |
| 145 | On Memorization in Probabilistic Deep Generative Models | NIPS | 2021 | [pdf] |
| 146 | Low-Rank Subspaces in GANs | NIPS | 2021 | [pdf] |
| 147 | Multimodal Few-Shot Learning with Frozen Language Models | NIPS | 2021 | [pdf] |
| 148 | Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation | NIPS | 2021 | [pdf] |
| 149 | Do Vision Transformers See Like Convolutional Neural Networks? | NIPS | 2021 | [pdf] |
| 150 | Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks | ICLR | 2022 | [pdf] |
| 151 | On the Measure of Intelligence | - | 2019 | [pdf] |
| 152 | Collapse by Conditioning: Training Class-conditional GANs with Limited Data | ICLR | 2022 | [pdf] |
| 153 | Regularizing Generative Adversarial Networks under Limited Data | CVPR | 2021 | [pdf] |
| 154 | Denoising Diffusion Probabilistic Models | NIPS | 2020 | [pdf] |
| 155 | Improved Denoising Diffusion Probabilistic Models | ICML | 2021 | [pdf] |
| 156 | Denoising Diffusion Implicit Models | ICLR | 2021 | [pdf] |
| 157 | TRGP: Trust Region Gradient Projection for Continual Learning | ICLR | 2022 | [pdf] |
| 158 | Rethinking the Representational Continuity: Towards Unsupervised Continual Learning | ICLR | 2022 | [pdf] |
| 159 | Diffusion Models Beat GANs on Image Synthesis | NIPS | 2021 | [pdf] |
| 160 | Classifier-free Diffusion Guidance | NIPS-W | 2021 | [pdf] |
| 161 | GLIDE: Towards Photorealistic Image Generation and Editing with Text-guided Diffusion Models | - | 2021 | [pdf] |
| 162 | VITON: An Image-based Virtual Try-on Network | CVPR | 2018 | [pdf] |
| 163 | Toward Characteristic-Preserving Image-based Virtual Try-On Network | ECCV | 2018 | [pdf] |
| 164 | VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization | CVPR | 2021 | [pdf] |
| 165 | SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation | CVPR | 2022 | [pdf] |
| 166 | Deblur-NeRF: Neural Radiance Fields from Blurry Images | CVPR | 2022 | [pdf] |
| 167 | Fashion Attribute-to-Image Synthesis Using Attention-based Generative Adversarial Network | WACV | 2019 | [pdf] |
| 168 | Attribute Manipulaiton Generative Adversarial Networks for Fashion Images | ICCV | 2019 | [pdf] |
| 169 | FlexIT: Towards Flexible Semantic Image Translation | CVPR | 2022 | [pdf] |
| 170 | Point-NeRF: Point-based Neural Radiance Fields | CVPR | 2022 | [pdf] |
| 171 | CLIP-Event: Connecting Text and Images with Event Structures | CVPR | 2022 | [pdf] |
| 172 | InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering | CVPR | 2022 | [pdf] |
| 173 | StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis | CVPR | 2022 | [pdf] |
| 174 | CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks | NIPS | 2021 | [pdf] |
| 175 | Blended Diffusion for Text-driven Editing of Natural Images | CVPR | 2022 | [pdf] |
| 176 | Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts | - | 2022 | [pdf] |
| 177 | BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation | ICML | 2022 | [pdf] |
| 178 | Vision-Language Pre-Training with Triple Contrastive Learning | CVPR | 2022 | [pdf] |
| 179 | Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks | - | 2022 | [pdf] |
| 180 | Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding | - | 2022 | [pdf] |
| 181 | Prompt-to-Prompt Image Editing with Cross Attention Control | - | 2022 | [pdf] |
| 182 | An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion | - | 2022 | [pdf] |
| 183 | Video Diffusion Models | - | 2022 | [pdf] |
| 184 | Diffusion Probabilistic Modeling for Video Generation | - | 2022 | [pdf] |
| 185 | High-resolution Image Synthesis with Latent Diffusion Models | CVPR | 2022 | [pdf] |
| 186 | Visual Prompting via Image Inpainting | NeurIPS | 2022 | [pdf] |
| 187 | On Distillation of Guided Diffusion Models | - | 2022 | [pdf] |
| 188 | Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance | - | 2022 | [pdf] |
| 189 | eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers | - | 2022 | [pdf] |
| 190 | Any-resolution Training for High-resolution Image Synthesis | ECCV | 2022 | [pdf] |
| 191 | Variational Diffusion Models | NeurIPS | 2021 | [pdf] |
| 192 | Elucidating the Design Space of Diffusion-Based Generative Models | NeurIPS | 2022 | [pdf] |
| 193 | Visual Prompt Tuning for Generative Transfer Learning | - | 2022 | [pdf] |
| 194 | The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation | - | 2022 | [pdf] |
| 195 | Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners | NeurIPS | 2022 | [pdf] |
| 196 | DreamFusion: Text-to-3D Using 2D Diffusion | ICLR | 2023 | [pdf] |
| 197 | Sketch-Guided Text-to-Image Diffusion Models | - | 2023 | [pdf] |
| 198 | Dataset Distillation by Matching Training Trajectories | CVPR | 2022 | [pdf] |
| 199 | Is synthetic data from generative models ready for image recognition? | ICLR | 2023 | [pdf] |
| 200 | Generative Models as a Data Source for Multiview Representation Learning | ICLR | 2022 | [pdf] |
| 201 | Scalable Diffusion Models with Transformers | - | 2022 | [pdf] |
| 202 | CAFE: Learning to Condense Dataset by Aligning Features | CVPR | 2022 | [pdf] |
| 203 | Learning to Learn with Generative Models of Neural Network Checkpoints | - | 2022 | [pdf] |
| 204 | Synthesizing Informative Training Samples with GAN | NeurIPSW | 2022 | [pdf] |
| 205 | Sliced Score Matching: A Scalable Approach to Density and Score Estimation | UAI | 2019 | [pdf] |
| 206 | Generative Modeling by Estimating Gradients of the Data Distribution | NeurIPS | 2019 | [pdf] |
| 207 | Dataset Distillation via Factorization | NeurIPS | 2022 | [pdf] |
| 208 | Visual Classification via Description from Large Language Models | ICLR | 2023 | [pdf] |
| 209 | Training Language Models to Follow Instructions from Human Feedback | - | 2022 | [pdf] |
| 210 | Adding Conditional Control to Text-to-Image Diffusion Models | - | 2023 | [pdf] |
| 211 | LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention | - | 2023 | [pdf] |
| 212 | BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models | - | 2023 | [pdf] |
| 213 | InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning | - | 2023 | [pdf] |
| 214 | Diffusion Models already have a Semantic Latent Space | ICLR | 2023 | [pdf] |
| 215 | Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation | ICLR | 2022 | [pdf] |
| 216 | Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer | NeurIPS | 2021 | [pdf] |
| 217 | Any-to-Any Generation via Composable Diffusion | - | 2023 | [pdf] |
| 218 | Too Large; Data Reduction for Vision-Language Pre-Training | - | 2023 | [pdf] |
| 219 | Controllable Text-to-Image Generation with GPT-4 | - | 2023 | [pdf] |
| 220 | VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners | - | 2023 | [pdf] |
| 221 | Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning | CVPR | 2023 | [pdf] |
| 222 | Visual Instruction Tuning | - | 2023 | [pdf] |
| 223 | PaLM-E: An Embodied Multimodal Language Model | - | 2023 | [pdf] |
| 224 | Flamingo: a Visual Language Model for Few-Shot Learning | NeurIPS | 2022 | [pdf] |
| 225 | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models | NeurIPS | 2022 | [pdf] |
| 226 | SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking | - | 2023 | [pdf] |
| 227 | Object-centric Learning with Cyclic Walks between Parts and Whole | NeurIPS | 2023 | [pdf] |
| 228 | Characterizing the Impacts of Semi-supervised Learning for Weak Supervision | NeurIPS | 2023 | [pdf] |
| 229 | Brain Decoding: Toward Real-time Reconstruction of Visual Perception | - | 2023 | [pdf] |
| 230 | One-step Diffusion with Distribution Matching Distillation | - | 2023 | [pdf] |
| 231 | Analyzing and Improving the Training Dynamics of Diffusion Models | - | 2023 | [pdf] |
| 232 | Common Diffusion Noise Schedules and Sample Steps are Flawed | - | 2023 | [pdf] |
| 233 | On the Importance of Noise Scheduling for Diffusion Models | - | 2023 | [pdf] |
| 234 | StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis | - | 2023 | [pdf] |