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 [BibTeX] [Marc21]
Knowledge Transfer with Jacobian Matching
Type of publication: Conference paper
Publication status: Published
Booktitle: Proceedings of the International Conference on Machine Learning
Year: 2018
Crossref: Srinivas_Idiap-RR-04-2018:
URL: http://proceedings.mlr.press/v...
Abstract: Classical distillation methods transfer representations from a “teacher” neural network to a “student” network by matching their output activations. Recent methods also match the Jacobians, or the gradient of output activations with the input. However, this involves making some ad hoc decisions, in particular, the choice of the loss function. In this paper, we first establish an equivalence between Jacobian matching and distillation with input noise, from which we derive appropriate loss functions for Jacobian matching. We then rely on this analysis to apply Jacobian matching to transfer learning by establishing equivalence of a recent transfer learning procedure to distillation. We then show experimentally on standard image datasets that Jacobian-based penalties improve distillation, robustness to noisy inputs, and transfer learning.
Projects Idiap
Authors Srinivas, Suraj
Fleuret, Francois
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