37 citations · 106 across the 17 of their papers we have counts for
4 papers · 1 filter
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift
Lin Li, Yifei Wang, Chawin Sitawarin +1
Existing works have made great progress in improving adversarial robustness, but typically test their method only on data from the same distribution as the training data, i.e. in-d…
A Comprehensive Assessment Benchmark for Rigorously Evaluating Deep Learning Image Classifiers
Michael W. Spratling
Reliable and robust evaluation methods are a necessary first step towards developing machine learning models that are themselves robust and reliable. Unfortunately, current evaluat…
When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review
Maxime Fontana, Michael Spratling, Miaojing Shi
Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple ou…
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization
Lin Li, Michael Spratling
Adversarial training is widely used to improve the robustness of deep neural networks to adversarial attack. However, adversarial training is prone to overfitting, and the cause is…