38 citations · 79 across the 5 of their papers we have counts for
10 papers
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
Micah Goldblum, Hossein Souri, Renkun Ni +10
Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an…
Can domain adaptation make object recognition work for everyone?
Viraj Prabhu, Ramprasaath R. Selvaraju, Judy Hoffman +1
Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperf…
UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Models
Arvindkumar Krishnakumar, Viraj Prabhu, Sruthi Sudhakar +1
Deep learning models have been shown to learn spurious correlations from data that sometimes lead to systematic failures for certain subpopulations. Prior work has typically diagno…
SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
Viraj Prabhu, Shivam Khare, Deeksha Kartik +1
Many existing approaches for unsupervised domain adaptation (UDA) focus on adapting under only data distribution shift and offer limited success under additional cross-domain label…
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko +1
Generalizing deep neural networks to new target domains is critical to their real-world utility. In practice, it may be feasible to get some target data labeled, but to be cost-eff…
Open Set Medical Diagnosis
Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso +4
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they ha…