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Generative Models

7,221 papers

Papers per year

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Papers

InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs ICML 2020
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors ICML 2020
Hallucinative Topological Memory for Zero-Shot Visual Planning ICML 2020
Optimal transport mapping via input convex neural networks ICML 2020
Reliable Fidelity and Diversity Metrics for Generative Models ICML 2020
PolyGen: An Autoregressive Generative Model of 3D Meshes ICML 2020
Semi-Supervised StyleGAN for Disentanglement Learning ICML 2020
Non-Autoregressive Neural Text-to-Speech ICML 2020
Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks ICML 2020
DeepCoDA: personalized interpretability for compositional health data ICML 2020
Implicit Generative Modeling for Efficient Exploration ICML 2020
Normalizing Flows on Tori and Spheres ICML 2020
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures ICML 2020
ControlVAE: Controllable Variational Autoencoder ICML 2020
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation ICML 2020
Deep Gaussian Markov Random Fields ICML 2020
Bridging the Gap Between f-GANs and Wasserstein GANs ICML 2020
Learning disconnected manifolds: a no GAN’s land ICML 2020
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions ICML 2020
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching ICML 2020
When deep denoising meets iterative phase retrieval ICML 2020
Amortized Population Gibbs Samplers with Neural Sufficient Statistics ICML 2020
Understanding and Stabilizing GANs’ Training Dynamics Using Control Theory ICML 2020
Energy-Based Processes for Exchangeable Data ICML 2020
Improving Molecular Design by Stochastic Iterative Target Augmentation ICML 2020