1 citations · 1 across the 6 of their papers we have counts for
6 papers
Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization
Jungsoo Lee, Debasmit Das, Jaegul Choo +1
Test-time adaptation (TTA) methods, which generally rely on the model's predictions (e.g., entropy minimization) to adapt the source pretrained model to the unlabeled target domain…
Progressive Random Convolutions for Single Domain Generalization
Seokeon Choi, Debasmit Das, Sungha Choi +3
Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains. Image augmentation based on Random…
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction
Shubhankar Borse, Debasmit Das, Hyojin Park +3
We present DejaVu, a novel framework which leverages conditional image regeneration as additional supervision during training to improve deep networks for dense prediction tasks su…
TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation
Debasmit Das, Shubhankar Borse, Hyojin Park +4
Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-…
Online Adaptive Personalization for Face Anti-spoofing
Davide Belli, Debasmit Das, Bence Major +1
Face authentication systems require a robust anti-spoofing module as they can be deceived by fabricating spoof images of authorized users. Most recent face anti-spoofing methods re…
Domain Agnostic Few-shot Learning for Speaker Verification
Seunghan Yang, Debasmit Das, Janghoon Cho +2
Deep learning models for verification systems often fail to generalize to new users and new environments, even though they learn highly discriminative features. To address this pro…