activity
20202024
most citedYour Classifier can Secretly Suffice Multi-Source Domain Adaptation

21 citations · 38 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CC2024

You Can't Solve These Super Mario Bros. Levels: Undecidable Mario Games

MIT Hardness Group, Erik D. Demaine, Lilly Hall +3

We prove RE-completeness (and thus undecidability) of several 2D games in the Super Mario Bros. platform video game series: the New Super Mario Bros. series (original, Wii, U, and…

cs.CV2023

Geometry-biased Transformers for Novel View Synthesis

Naveen Venkat, Mayank Agarwal, Maneesh Singh +1

We tackle the task of synthesizing novel views of an object given a few input images and associated camera viewpoints. Our work is inspired by recent 'geometry-free' approaches whe…

cs.LG2021★ 21 cited

Your Classifier can Secretly Suffice Multi-Source Domain Adaptation

Naveen Venkat, Jogendra Nath Kundu, Durgesh Kumar Singh +2

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 m…

cs.LG2020

Class-Incremental Domain Adaptation

Jogendra Nath Kundu, Rahul Mysore Venkatesh, Naveen Venkat +2

We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for learning…

cs.CV2020

Universal Source-Free Domain Adaptation

Jogendra Nath Kundu, Naveen Venkat, Rahul M +1

There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domai…

cs.CV2020★ 17 cited

Towards Inheritable Models for Open-Set Domain Adaptation

Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur +2

There has been a tremendous progress in Domain Adaptation (DA) for visual recognition tasks. Particularly, open-set DA has gained considerable attention wherein the target domain c…