263 citations · 806 across the 42 of their papers we have counts for
19 papers · 1 filter
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data
Tiantian Fang, Ruoyu Sun, Alex Schwing
Generative adversarial nets (GANs) have been remarkably successful at learning to sample from distributions specified by a given dataset, particularly if the given dataset is reaso…
On the Importance of Gradient Norm in PAC-Bayesian Bounds
Itai Gat, Yossi Adi, Alexander Schwing +1
Generalization bounds which assess the difference between the true risk and the empirical risk, have been studied extensively. However, to obtain bounds, current techniques use str…
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations
Kai Yan, Alexander G. Schwing, Yu-Xiong Wang
Although reinforcement learning has found widespread use in dense reward settings, training autonomous agents with sparse rewards remains challenging. To address this difficulty, p…
Equivariance Discovery by Learned Parameter-Sharing
Raymond A. Yeh, Yuan-Ting Hu, Mark Hasegawa-Johnson +1
Designing equivariance as an inductive bias into deep-nets has been a prominent approach to build effective models, e.g., a convolutional neural network incorporates translation eq…
Perceptual Score: What Data Modalities Does Your Model Perceive?
Itai Gat, Idan Schwartz, Alexander Schwing
Machine learning advances in the last decade have relied significantly on large-scale datasets that continue to grow in size. Increasingly, those datasets also contain different da…
Robustifying Adversarial Training to the Union of Perturbation Models
Ameya D. Patil, Michael Tuttle, Alexander G. Schwing +1
Classical adversarial training (AT) frameworks are designed to achieve high adversarial accuracy against a single attack type, typically norm-bounded perturbations. R…