Deep generative models using Generative Adversarial Networks(GANs).
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Updated
Aug 6, 2020 - Jupyter Notebook
Deep generative models using Generative Adversarial Networks(GANs).
Comparison of standard autoencoder and variational autoencoder (VAE) as deep generative models
Mini-project for my CST Part III Representation Learning on Graphs and Networks (L45) module
PyTorch implementation of image inpainting technique as proposed in paper "Sementic Image Inpainting with Deep Generative Modes by R.A. Yeh et al."
Implementation of NICE (Non-linear Independent Components Estimation) in TF Keras
Exercises from IT3030 V20
Deep Generative Models with clean and well-annotated PyTorch re-implementation
This GitHub repository showcases my bachelor thesis which is focused on exploring the application and comparison of various deep generative models for synthetic image augmentation in manufacturing domain.
Facial Unpaired Image-to-Image Translation with (Self-Attention) Conditional Cycle-Consistent Generative Adversarial Networks
Unofficial PyTorch implementation of IODINE https://arxiv.org/abs/1903.00450
This is the official implementation of RL-Chord (TNNLS).
A basic PyTorch implementation of the Collaborative Sampling in Generative Adversarial Networks
PyTorch Implementations of Popular Deep Generative Models.
The official implementation of the manuscript Learning the complexity of urban mobility with deep generative collaboration network.
Pytorch implementation of WIPA: Super-resolution of very low-resolution face images with a Wavelet Integrated, Identity Preserving, Adversarial Network.
Generative Autoregressive, Normalized Flows, VAEs, Score-based models (GANVAS)
Code, documentation, and tutorials for the DGD model trained on bulk RNA-Seq data.
PyTorch Implementation of V-objective Diffusion Probabilistic Models with Classifier-free Guidance
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