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Durgesh Singh

2 accepted papers

2023

Supercm: Revisiting Clustering for Semi-Supervised Learning

ICASSP 2023accepted

The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often resulting in models with complex training strategies to obtain the desired results. In this work, we instead propose a…

Cited by 0SourceScholar
2020

Your Classifier can Secretly Suffice Multi-Source Domain Adaptation

NeurIPS 2020poster

Multi-Source Domain Adaptation (MSDA) deals with the transfer of task knowledge from multiple labeled source domains to an unlabeled target domain, under a domain-shift. Existing methods aim to minimize this domain-shift using auxiliary distribution alignment objectives. In this work, we present a d…

Cited by 97SourcePDFScholar