3.5k citations · 9.4k across the 54 of their papers we have counts for
8 papers · 2 filters
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…
Temporal Action Detection with Multi-level Supervision
Baifeng Shi, Qi Dai, Judy Hoffman +3
Training temporal action detection in videos requires large amounts of labeled data, yet such annotation is expensive to collect. Incorporating unlabeled or weakly-labeled data to…
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…
Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents
Samyak Datta, Oleksandr Maksymets, Judy Hoffman +3
Recent work has presented embodied agents that can navigate to point-goal targets in novel indoor environments with near-perfect accuracy. However, these agents are equipped with i…
TIDE: A General Toolbox for Identifying Object Detection Errors
Daniel Bolya, Sean Foley, James Hays +1
We introduce TIDE, a framework and associated toolbox for analyzing the sources of error in object detection and instance segmentation algorithms. Importantly, our framework is app…
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
Prithvijit Chattopadhyay, Yogesh Balaji, Judy Hoffman
We introduce Domain-specific Masks for Generalization, a model for improving both in-domain and out-of-domain generalization performance. For domain generalization, the goal is to…
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses
Fu Lin, Rohit Mittapalli, Prithvijit Chattopadhyay +2
Convolutional Neural Networks have been shown to be vulnerable to adversarial examples, which are known to locate in subspaces close to where normal data lies but are not naturally…
Analyzing Visual Representations in Embodied Navigation Tasks
Erik Wijmans, Julian Straub, Dhruv Batra +3
Recent advances in deep reinforcement learning require a large amount of training data and generally result in representations that are often over specialized to the target task. I…