5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.CV2021★ 5 cited
AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation
Viraj Prabhu, Shivam Khare, Deeksha Kartik +1
Most modern approaches for domain adaptive semantic segmentation rely on continued access to source data during adaptation, which may be infeasible due to computational or privacy…
cs.LG2021★ 1 cited
Unsupervised Class-Incremental Learning Through Confusion
Shivam Khare, Kun Cao, James Rehg
While many works on Continual Learning have shown promising results for mitigating catastrophic forgetting, they have relied on supervised training. To successfully learn in a labe…
cs.CV2020
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…