awesome-dgm-papers
github.com/liang-hou/awesome-dgm-papers ↗awesome deep generative models papers
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ICLR 2020ICML 2020NeurIPS 2020ICLR 2021ICML 2021NeurIPS 2021 (OpenReview)
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ICLR 2020
- A Closer Look at the Optimization Landscapes of Generative Adversarial NetworksPoster
- Adversarial Lipschitz RegularizationPoster
- AE-OT: A New Generative Model based on Extended Semi-Discrete Optimal TransportPoster
- Consistency Regularization for Generative Adversarial NetworksPoster
- Difference-Seeking Generative Adversarial Network--Unseen Sample GenerationPoster
- From Variational to Deterministic AutoencodersPoster
NeurIPS 2020
- A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
- A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
- Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance
- Autoencoders that don't overfit towards the Identity
- Autoregressive Score Matching
- Bi-level Score Matching for Learning Energy-based Latent Variable Models
ICML 2021
- Adversarial Purification with Score-based Generative Models
- A Language for Counterfactual Generative Models
- A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention
- Autoencoder Image Interpolation by Shaping the Latent Space
- Autoencoding Under Normalization Constraints
- Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
ICLR 2021
- A Geometric Analysis of Deep Generative Image Models and Its ApplicationsPoster
- A Good Image Generator Is What You Need for High-Resolution Video SynthesisSpotlight
- Anytime Sampling for Autoregressive Models via Ordered AutoencodingPoster
- Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View SynthesisPoster
- CcGAN: Continuous Conditional Generative Adversarial Networks for Image GenerationPoster
- Conditional Generative Modeling via Learning the Latent SpacePoster
NeurIPS 2021 (OpenReview)
- Alias-Free Generative Adversarial NetworksOral
- A Unified View of cGANs with and without ClassifiersPoster
- A Variational Perspective on Diffusion-Based Generative Models and Score MatchingSpotlight
- BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face GenerationPoster
- Breaking the Dilemma of Medical Image-to-image TranslationSpotlight
- Bridging Explicit and Implicit Deep Generative Models via Neural Stein EstimatorsPoster
ICML 2020
- AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
- Bridging the Gap Between f-GANs and Wasserstein GANs
- ControlVAE: Controllable Variational Autoencoder
- Distribution Augmentation for Generative Modeling
- Do GANs always have Nash equilibria?
- Eliminating the Invariance on the Loss Landscape of Linear Autoencoders
Showing a sample of 249 resources. View the full list on GitHub →