안녕하세요. 모카의 머신러닝 입니다.
NIPS 2020에 accept 된 GAN 논문 리스트 입니다.
- ColdGANs: Taming Language GANs with Cautious Sampling Strategies paper
- HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis paper
- GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators paper
- GANSpace: Discovering Interpretable GAN Controls paper code
- Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample paper
- Distributional Robustness with IPMs and links to Regularization and GANs paper
- TaylorGAN: Neighbor-Augmented Policy Update for Sample-Efficient Natural Language Generation paper
- DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs paper
- GramGAN: Deep 3D Texture Synthesis From 2D Exemplars paper
- Instance Selection for GANs paper
- COT-GAN: Generating Sequential Data via Causal Optimal Transport paper
- Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data paper
- Improving GAN Training with Probability Ratio Clipping and Sample Reweighting paper
- BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images paper
- Teaching a GAN What Not to Learn paper
- GAN Memory with No Forgetting paper code
- PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals paper
- Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling paper
- Towards a Better Global Loss Landscape of GANs paper
- Differentiable Augmentation for Data-Efficient GAN Training paper code
- ContraGAN: Contrastive Learning for Conditional Image Generation paper
- CircleGAN: Generative Adversarial Learning across Spherical Circles paper
- Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN paper
- Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples paper
- Training Generative Adversarial Networks with Limited Data paper
- Learning Semantic-aware Normalization for Generative Adversarial Networks paper
- Training Generative Adversarial Networks by Solving Ordinary Differential Equations paper
- Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation paper
- A Decentralized Parallel Algorithm for Training Generative Adversarial Nets paper