VGILab-generative-model-study

Participants

Paper

Deep Learning Basics

1. Deep Residual Learning for Image Recognition (ECCV 2015) - [Paper] [Material]

GAN

1. Generative Adversarial Networks (NeuRIPS 2014) - [Paper] [Material]

2. Unsupervised Representation Learning with Deep Convolutional Generative Adersarial Networks (Arxiv) - [Paper] [Material]

3. Progressive Growing of GANs for Improved Quality, Stability, and Variation (ICLR 2018) - [Paper] [Material]

4. Self-Attention Generative Adversarial Networks (ICML 2019) - [Paper] [Material]

5. Large Scale GAN Training for High Fidelity Natural Image Synthesis (ICLR 2019) - [Paper] [Material]

6. A Style-Based Generator Architecture for Generative Adversarial Networks (CVPR 2019) - [Paper] [Material]

7. Analyzing and Improving the Image Quality of StyleGAN (CVPR 2022) - [Paper] [Material]

Diffusion Model

1. Denoising Diffusion Probabilistic Models (NeuRIPS 2020) - [Paper] [Material]

2. High-Resolution Image Synthesis with Latent Diffusion Models (CVPR 2022) - [Paper] [Material]

3. Denoising Diffusion Implicit Models (ICLR 2021) - [Paper] [Material]

4. Diffusion Models Beat GANs on Image Synthesis (NeuRIPS 2021 Spotlight) - [Paper] [Material]

5. Classifier-Free Diffusion Guidance (NeurIPS 2021: Workshop on Deep Generative Models and Downstream Applications) - [Paper] [Material]

6. Tackling the Generative Learning Trilemma with Denoising Diffusion GANs (ICLR 2022 Spotlight) - [Paper] [Material]

7. Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021 Oral) - [Paper] [Material]

8. Consistency Models (ICML 2023) - [Paper] [Material]

Other Topics

Codes