163 citations · 1.4k across the 139 of their papers we have counts for
5 papers · 2 filters
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases
Mazda Moayeri, Wenxiao Wang, Sahil Singla +1
We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues. Instead of requiring costly changes to one's data or model traini…
Invariant Learning via Diffusion Dreamed Distribution Shifts
Priyatham Kattakinda, Alexander Levine, Soheil Feizi
Though the background is an important signal for image classification, over reliance on it can lead to incorrect predictions when spurious correlations between foreground and backg…
Towards Improved Input Masking for Convolutional Neural Networks
Sriram Balasubramanian, Soheil Feizi
The ability to remove features from the input of machine learning models is very important to understand and interpret model predictions. However, this is non-trivial for vision mo…
Core Risk Minimization using Salient ImageNet
Sahil Singla, Mazda Moayeri, Soheil Feizi
Deep neural networks can be unreliable in the real world especially when they heavily use spurious features for their predictions. Recently, Singla & Feizi (2022) introduced the Sa…
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes
Mazda Moayeri, Phillip Pope, Yogesh Balaji +1
While datasets with single-label supervision have propelled rapid advances in image classification, additional annotations are necessary in order to quantitatively assess how model…