37 citations · 106 across the 17 of their papers we have counts for
8 papers · 1 filter
The Importance of Anti-Aliasing in Tiny Object Detection
Jinlai Ning, Michael Spratling
Tiny object detection has gained considerable attention in the research community owing to the frequent occurrence of tiny objects in numerous critical real-world scenarios. Howeve…
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…
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation
Lin Li, Jianing Qiu, Michael Spratling
Deep neural networks are vulnerable to adversarial examples. Adversarial training (AT) is an effective defense against adversarial examples. However, AT is prone to overfitting whi…
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing
Lin Li, Michael Spratling
Deep neural networks can be easily fooled into making incorrect predictions through corruption of the input by adversarial perturbations: human-imperceptible artificial noise. So f…
Rethinking the backbone architecture for tiny object detection
Jinlai Ning, Haoyan Guan, Michael Spratling
Tiny object detection has become an active area of research because images with tiny targets are common in several important real-world scenarios. However, existing tiny object det…
Data Augmentation Alone Can Improve Adversarial Training
Lin Li, Michael Spratling
Adversarial training suffers from the issue of robust overfitting, which seriously impairs its generalization performance. Data augmentation, which is effective at preventing overf…