26 citations · 74 across the 14 of their papers we have counts for
6 papers · 1 filter
Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
Gina Wong, Joshua Gleason, Rama Chellappa +2
Learning models whose predictions are invariant under multiple environments is a promising approach for out-of-distribution generalization. Such models are trained to extract featu…
Learning to Prompt Your Domain for Vision-Language Models
Guoyizhe Wei, Feng Wang, Anshul Shah +1
Prompt learning has recently become a very efficient transfer learning paradigm for Contrastive Language Image Pretraining (CLIP) models. Compared with fine-tuning the entire encod…
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
Mahyar Fazlyab, Taha Entesari, Aniket Roy +1
To improve the robustness of deep classifiers against adversarial perturbations, many approaches have been proposed, such as designing new architectures with better robustness prop…
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning
Yuxin Wen, Jonas Geiping, Liam Fowl +4
Federated learning is particularly susceptible to model poisoning and backdoor attacks because individual users have direct control over the training data and model updates. At the…
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
Yogesh Balaji, Rama Chellappa, Soheil Feizi
Optimal Transport (OT) distances such as Wasserstein have been used in several areas such as GANs and domain adaptation. OT, however, is very sensitive to outliers (samples with la…
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue
Pirazh Khorramshahi, Hossein Souri, Rama Chellappa +1
Building on the success of deep learning, Generative Adversarial Networks (GANs) provide a modern approach to learn a probability distribution from observed samples. GANs are often…