74 citations · 135 across the 18 of their papers we have counts for
5 papers · 1 filter
Unsupervised Adaptation of Semantic Segmentation Models without Source Data
Sujoy Paul, Ansh Khurana, Gaurav Aggarwal
We consider the novel problem of unsupervised domain adaptation of source models, without access to the source data for semantic segmentation. Unsupervised domain adaptation aims t…
SITA: Single Image Test-time Adaptation
Ansh Khurana, Sujoy Paul, Piyush Rai +2
In Test-time Adaptation (TTA), given a source model, the goal is to adapt it to make better predictions for test instances from a different distribution than the source. Crucially,…
Reconstruction guided Meta-learning for Few Shot Open Set Recognition
Sayak Nag, Dripta S. Raychaudhuri, Sujoy Paul +1
In many applications, we are constrained to learn classifiers from very limited data (few-shot classification). The task becomes even more challenging if it is also required to ide…
Cross-domain Imitation from Observations
Dripta S. Raychaudhuri, Sujoy Paul, Jeroen van Baar +1
Imitation learning seeks to circumvent the difficulty in designing proper reward functions for training agents by utilizing expert behavior. With environments modeled as Markov Dec…
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul +2
Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain. However, most of these…