71 citations · 129 across the 21 of their papers we have counts for
8 papers · 1 filter
Hidden in the Noise: Two-Stage Robust Watermarking for Images
Kasra Arabi, Benjamin Feuer, R. Teal Witter +2
As the quality of image generators continues to improve, deepfakes become a topic of considerable societal debate. Image watermarking allows responsible model owners to detect and…
SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification
Benjamin Feuer, Jiawei Xu, Niv Cohen +3
Data curation is the problem of how to collect and organize samples into a dataset that supports efficient learning. Despite the centrality of the task, little work has been devote…
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
Chih-Hsuan Yang, Benjamin Feuer, Zaki Jubery +12
We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity. Curated from the iNaturalist platform and vetted to include only…
Exploring Dataset-Scale Indicators of Data Quality
Benjamin Feuer, Chinmay Hegde
Modern computer vision foundation models are trained on massive amounts of data, incurring large economic and environmental costs. Recent research has suggested that improving data…
Distributionally Robust Classification on a Data Budget
Benjamin Feuer, Ameya Joshi, Minh Pham +1
Real world uses of deep learning require predictable model behavior under distribution shifts. Models such as CLIP show emergent natural distributional robustness comparable to hum…
LiT Tuned Models for Efficient Species Detection
Andre Nakkab, Benjamin Feuer, Chinmay Hegde
Recent advances in training vision-language models have demonstrated unprecedented robustness and transfer learning effectiveness; however, standard computer vision datasets are im…
Caption supervision enables robust learners
Benjamin Feuer, Ameya Joshi, Chinmay Hegde
Vision language (VL) models like CLIP are robust to natural distribution shifts, in part because CLIP learns on unstructured data using a technique called caption supervision; the…
A Meta-Analysis of Distributionally-Robust Models
Benjamin Feuer, Ameya Joshi, Chinmay Hegde
State-of-the-art image classifiers trained on massive datasets (such as ImageNet) have been shown to be vulnerable to a range of both intentional and incidental distribution shifts…