← Learning Types
Deep Learning › Learning Types ›

Generative Models

1275 directly classified papers

Papers per year

Papers

Generative Classifiers as a Basis for Trustworthy Image Classification CVPR 2021
ReMix: Towards Image-to-Image Translation With Limited Data CVPR 2021
CoMoGAN: Continuous Model-Guided Image-to-Image Translation CVPR 2021
OSTeC: One-Shot Texture Completion CVPR 2021
SceneGen: Learning To Generate Realistic Traffic Scenes CVPR 2021
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer CVPR 2021
Blur, Noise, and Compression Robust Generative Adversarial Networks CVPR 2021
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification CVPR 2021
Instance-Conditioned GAN NIPS 2021
On the Power of Edge Independent Graph Models NIPS 2021
Learning to Compose Visual Relations NIPS 2021
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data NIPS 2021
Catch-A-Waveform: Learning to Generate Audio from a Single Short Example NIPS 2021
CogView: Mastering Text-to-Image Generation via Transformers NIPS 2021
Bounds all around: training energy-based models with bidirectional bounds NIPS 2021
Predicting Molecular Conformation via Dynamic Graph Score Matching NIPS 2021
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds NIPS 2021
Efficient and Accurate Gradients for Neural SDEs NIPS 2021
The Image Local Autoregressive Transformer NIPS 2021
On the Frequency Bias of Generative Models NIPS 2021
Evaluation Guidelines to Deal with Implicit Phenomena to Assess Factuality in Data-to-Text Generation ACL 2021
Keyword Augmentation via Generative Methods ACL 2021
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation ACL 2021
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval ACL 2021
Structured Denoising Diffusion Models in Discrete State-Spaces NIPS 2021