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pro vyhledávání: '"Bhambri, Suvaansh"'
Autor:
Sanyal, Sunandini, Asokan, Ashish Ramayee, Bhambri, Suvaansh, Kulkarni, Akshay, Kundu, Jogendra Nath, Babu, R. Venkatesh
Conventional Domain Adaptation (DA) methods aim to learn domain-invariant feature representations to improve the target adaptation performance. However, we motivate that domain-specificity is equally important since in-domain trained models hold cruc
Externí odkaz:
http://arxiv.org/abs/2308.14023
Robotic agents performing domestic chores by natural language directives are required to master the complex job of navigating environment and interacting with objects in the environments. The tasks given to the agents are often composite thus are cha
Externí odkaz:
http://arxiv.org/abs/2308.09387
Autor:
Kundu, Jogendra Nath, Bhambri, Suvaansh, Kulkarni, Akshay, Sarkar, Hiran, Jampani, Varun, Babu, R. Venkatesh
Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabeled target samples, either into one of the "known" categories or into a
Externí odkaz:
http://arxiv.org/abs/2210.15909
Autor:
Kundu, Jogendra Nath, Bhambri, Suvaansh, Kulkarni, Akshay, Sarkar, Hiran, Jampani, Varun, Babu, R. Venkatesh
The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains. Prior DA works show that pretext tasks could be used to mitigate this domain shift by learning domain invariant represen
Externí odkaz:
http://arxiv.org/abs/2207.13247
Autor:
Kundu, Jogendra Nath, Kulkarni, Akshay, Bhambri, Suvaansh, Mehta, Deepesh, Kulkarni, Shreyas, Jampani, Varun, Babu, R. Venkatesh
Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discriminability knowledge gathered from the labeled source data. However, the r
Externí odkaz:
http://arxiv.org/abs/2206.08009
Autor:
Kundu, Jogendra Nath, Kulkarni, Akshay, Bhambri, Suvaansh, Jampani, Varun, Babu, R. Venkatesh
Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting which considers a single labeled source domain against a compound of multi-modal unlabeled target data in order to generalize better on novel unseen domains. We hypo
Externí odkaz:
http://arxiv.org/abs/2202.04287
Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents. The 'interactive instruction following' task attempts to make progress towards building agents that jointly navi
Externí odkaz:
http://arxiv.org/abs/2012.03208