Nov 16, 2020 · In this paper, we introduce a holistic online dataset adaptation strategy for on-device deep models running in the wild by tackling three challenges of dataset ...
Duration: 14:43
Posted: Nov 14, 2020
Posted: Nov 14, 2020
Nov 16, 2020 · A holistic online dataset adaptation strategy for on-device deep models running in the wild by tackling three challenges of dataset adaption ...
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AIChallengeIoT 2020 ("Learning in the Wild: When, How, and What to Learn for On-Device Dataset Adaptation") [Link]. Best Paper Award Candidate. Jae Wook Lee ...
Learning in the Wild: When, How, and What to Learn for On-Device Dataset Adaptation. Conference Paper. Nov 2020. Seulki Lee · Shahriar Nirjon · View · BFree ...
Data-free learning for student networks is a new paradigm for solving users' anxiety caused by the privacy problem of using original training data.
In this case study, deep reinforcement learning (DRL) is applied to an agent in a multi-player business simulation video game with steadily increasing ...
Jun 10, 2024 · In this paper, we propose TinyTrain, an on-device training approach that drastically reduces training time by selectively updating parts of the model.
[7] S. Lee and S. Nirjon, “Learning in the wild: When, how, and what to learn for on-device dataset adaptation,” in Proceedings of the ...
Learning in the Wild: When, How, and What to Learn for On-Device Dataset Adaptation · Supervised and Unsupervised Transfer Learning for Activity Recognition from ...