Learning to Learn with Variational Information Bottleneck for Domain Generalization-Reference-Cited by-同舟云学术

Learning to Learn with Variational Information Bottleneck for Domain Generalization

Published:2020 Issue: Volume: Page:200-216
ISSN:0302-9743
Container-title:Computer Vision – ECCV 2020
language:
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Author:

Du Yingjun^ORCID,Xu Jun,Xiong Huan,Qiu Qiang,Zhen Xiantong,Snoek Cees G. M.,Shao Ling

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-58607-2_12

Reference53 articles.

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2. Amjad, R.A., Geiger, B.C.: Learning representations for neural network-based classification using the information bottleneck principle. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)

3. Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Advances in Neural Information Processing Systems (2016)

4. Balaji, Y., Sankaranarayanan, S., Chellappa, R.: MetaReg: towards domain generalization using meta-regularization. In: Advances in Neural Information Processing Systems, pp. 998–1008 (2018)

5. Bertinetto, L., Henriques, J.F., Valmadre, J., Torr, P.H.S., Vedaldi, A.: Learning feed-forward one-shot learners. In: Advances in Neural Information Processing Systems (2016)

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