11 citations · 13 across the 11 of their papers we have counts for
4 papers · 1 filter
Alignment and Adversarial Robustness: Are More Human-Like Models More Secure?
Blaine Hoak, Kunyang Li, Patrick McDaniel
A small but growing body of work has shown that machine learning models which better align with human vision have also exhibited higher robustness to adversarial examples, raising…
Err on the Side of Texture: Texture Bias on Real Data
Blaine Hoak, Ryan Sheatsley, Patrick McDaniel
Bias significantly undermines both the accuracy and trustworthiness of machine learning models. To date, one of the strongest biases observed in image classification models is text…
On Synthetic Texture Datasets: Challenges, Creation, and Curation
Blaine Hoak, Patrick McDaniel
The influence of textures on machine learning models has been an ongoing investigation, specifically in texture bias/learning, interpretability, and robustness. However, due to the…
Explorations in Texture Learning
Blaine Hoak, Patrick McDaniel
In this work, we investigate \textit{texture learning}: the identification of textures learned by object classification models, and the extent to which they rely on these textures.…