9 citations · 22 across the 12 of their papers we have counts for
4 papers · 1 filter
Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data
Samarth Mishra, Rameswar Panda, Cheng Perng Phoo +5
Pre-training models on Imagenet or other massive datasets of real images has led to major advances in computer vision, albeit accompanied with shortcomings related to curation cost…
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Dina Bashkirova, Dan Hendrycks, Donghyun Kim +5
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. This over-estimates future accuracy on o…
Effectively Leveraging Attributes for Visual Similarity
Samarth Mishra, Zhongping Zhang, Yuan Shen +3
Measuring similarity between two images often requires performing complex reasoning along different axes (e.g., color, texture, or shape). Insights into what might be important for…
Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency
Samarth Mishra, Kate Saenko, Venkatesh Saligrama
Most modern unsupervised domain adaptation (UDA) approaches are rooted in domain alignment, i.e., learning to align source and target features to learn a target domain classifier u…