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Generative Models

6381 directly classified papers

Papers per year

Papers

Learning a Neural 3D Texture Space From 2D Exemplars CVPR 2020
Neural Contours: Learning to Draw Lines From 3D Shapes CVPR 2020
Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content CVPR 2020
MISC: Multi-Condition Injection and Spatially-Adaptive Compositing for Conditional Person Image Synthesis CVPR 2020
Diverse Image Generation via Self-Conditioned GANs CVPR 2020
Probabilistic Video Prediction From Noisy Data With a Posterior Confidence CVPR 2020
HCNAF: Hyper-Conditioned Neural Autoregressive Flow and its Application for Probabilistic Occupancy Map Forecasting CVPR 2020
Conditional Gaussian Distribution Learning for Open Set Recognition CVPR 2020
Episode-Based Prototype Generating Network for Zero-Shot Learning CVPR 2020
Adversarial Feature Hallucination Networks for Few-Shot Learning CVPR 2020
Through Fog High-Resolution Imaging Using Millimeter Wave Radar CVPR 2020
MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model CVPR 2020
GIFnets: Differentiable GIF Encoding Framework CVPR 2020
Generalized Zero-Shot Learning via Over-Complete Distribution CVPR 2020
Self-Supervised Domain-Aware Generative Network for Generalized Zero-Shot Learning CVPR 2020
Fast and Flexible Temporal Point Processes with Triangular Maps NIPS 2020
Learning Manifold Implicitly via Explicit Heat-Kernel Learning NIPS 2020
Neural Networks with Recurrent Generative Feedback NIPS 2020
Improving Inference for Neural Image Compression NIPS 2020
Robust compressed sensing using generative models NIPS 2020
Sinkhorn Natural Gradient for Generative Models NIPS 2020
Network-to-Network Translation with Conditional Invertible Neural Networks NIPS 2020
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions NIPS 2020
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators NIPS 2020
Teaching a GAN What Not to Learn NIPS 2020