The goal of domain generalization (DG) is to enhance the generalization capability of the
model learned from a source domain to other unseen domains. The recently developed …

Federated Learning (FL) is a promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …

Abstract Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of
the loss landscape and generalization, has demonstrated a significant performance boost …

Abstract Model-based deep learning has achieved astounding successes due in part to the
availability of large-scale real-world data. However, processing such massive amounts of …

Modern deep neural networks (DNNs) have achieved state-of-the-art performances but are
typically over-parameterized. The over-parameterization may result in undesirably large …

Deep neural networks often suffer from poor generalization caused by complex and non-
convex loss landscapes. One of the popular solutions is Sharpness-Aware Minimization …

Recently, flat minima are proven to be effective for improving generalization and sharpness-
aware minimization (SAM) achieves state-of-the-art performance. Yet the current definition of …

Backdoor defense, which aims to detect or mitigate the effect of malicious triggers introduced
by attackers, is becoming increasingly critical for machine learning security and integrity …

Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods,
have been shown to improve a neural network's generalization performance over stochastic …

Sharpness of minima is a promising quantity that can correlate with generalization in deep
networks and, when optimized during training, can improve generalization. However …