3 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 3 cited
Adversarial Attacks on Foundational Vision Models
Nathan Inkawhich, Gwendolyn McDonald, Ryan Luley
Rapid progress is being made in developing large, pretrained, task-agnostic foundational vision models such as CLIP, ALIGN, DINOv2, etc. In fact, we are approaching the point where…
cs.CV2023
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Jingyang Zhang, Nathan Inkawhich, Randolph Linderman +3
Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which…
cs.LG2021★ 3 cited
Active Learning Under Malicious Mislabeling and Poisoning Attacks
Jing Lin, Ryan Luley, Kaiqi Xiong
Deep neural networks usually require large labeled datasets for training to achieve state-of-the-art performance in many tasks, such as image classification and natural language pr…