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pro vyhledávání: '"Sanyal, Sunandini"'
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
In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL). The goal is to update the model on continually changing domains while preserving domain-specific knowledg
Externí odkaz:
http://arxiv.org/abs/2304.07560
Machine learning models deployed as a service (MLaaS) are susceptible to model stealing attacks, where an adversary attempts to steal the model within a restricted access framework. While existing attacks demonstrate near-perfect clone-model performa
Externí odkaz:
http://arxiv.org/abs/2204.11022
Autor:
Mandal, Ratna, Agarwal, Nitin, Nandi, Subrata, Das, Projan, Anvit, Aniket, Sanyal, Sunandini, Saha, Sujoy
Publikováno v:
2015 IEEE International Conference on Pervasive Computing & Communication Workshops (PerCom Workshops); 2015, p276-279, 4p