Jakubovitz, D;
Giryes, R;
Rodrigues, MRD;
(2020)
Lautum Regularization for Semi-Supervised Transfer Learning.
In:
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
(pp. pp. 763-767).
IEEE: Seoul, Korea (South).
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Abstract
Transfer learning is a very important 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 a novel information theoretic approach 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 the network training on the source dataset. Our theory suggests that one may improve the transferability of a deep neural network by imposing a Lautum information based regularization that relates the network weights to the target data. We demonstrate the effectiveness of the proposed approach in various transfer learning experiments.
| Type: | Proceedings paper |
|---|---|
| Title: | Lautum Regularization for Semi-Supervised Transfer Learning |
| Event: | 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) |
| Location: | Seoul, SOUTH KOREA |
| Dates: | 27 October 2019 - 02 November 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICCVW.2019.00100 |
| Publisher version: | https://doi.org/10.1109/ICCVW.2019.00100 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Transfer Learning, Semi Supervised Learning, Information Theory |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10109748 |
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