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- W4367189931 abstract "In this work, we propose a general framework called Concept-Monitor to help demystify the black-box DNN training processes automatically using a novel unified embedding space and concept diversity metric. Concept-Monitor enables human-interpretable visualization and indicators of the DNN training processes and facilitates transparency as well as deeper understanding on how DNNs develop along the during training. Inspired by these findings, we also propose a new training regularizer that incentivizes hidden neurons to learn diverse concepts, which we show to improve training performance. Finally, we apply Concept-Monitor to conduct several case studies on different training paradigms including adversarial training, fine-tuning and network pruning via the Lottery Ticket Hypothesis" @default.
- W4367189931 created "2023-04-28" @default.
- W4367189931 creator A5007306953 @default.
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- W4367189931 date "2023-04-26" @default.
- W4367189931 modified "2023-09-25" @default.
- W4367189931 title "Concept-Monitor: Understanding DNN training through individual neurons" @default.
- W4367189931 doi "https://doi.org/10.48550/arxiv.2304.13346" @default.
- W4367189931 hasPublicationYear "2023" @default.
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