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- W4380558421 abstract "Transfer learning is a valuable tool in deep learning as it allows propagating information from one source dataset to another target dataset, especially in the case of a small number of training examples in the latter. Yet, discrepancies between the underlying distributions of the source and target data are commonplace and are known to have a substantial impact on algorithm performance. In this work we suggest novel information-theoretic approaches for the analysis of the performance of deep neural networks in the context of transfer learning. We focus on the task of semi-supervised transfer learning, in which unlabeled samples from the target dataset are available during network training on the source dataset. Our theory suggests that one may improve the transferability of a deep neural network by incorporating regularization terms on the target data based on information-theoretic quantities, namely the Mutual Information and the Lautum Information. We demonstrate the effectiveness of the proposed approaches in various semi-supervised transfer learning experiments." @default.
- W4380558421 created "2023-06-14" @default.
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- W4380558421 date "2023-06-11" @default.
- W4380558421 modified "2023-09-27" @default.
- W4380558421 title "An information-Theoretic Approach to Semi-supervised Transfer Learning" @default.
- W4380558421 doi "https://doi.org/10.48550/arxiv.2306.06731" @default.
- W4380558421 hasPublicationYear "2023" @default.
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