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arXiv · 1811.12273

On the Transferability of Representations in Neural Networks Between Datasets and Tasks

Haytham M. Fayek·Lawrence Cavedon·Hong Ren Wu
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Abstract

Deep networks, composed of multiple layers of hierarchical distributed representations, tend to learn low-level features in initial layers and transition to high-level features towards final layers. Paradigms such as transfer learning, multi-task learning, and continual learning leverage this notion of generic hierarchical distributed representations to share knowledge across datasets and tasks. Herein, we study the layer-wise transferability of representations in deep networks across a few datasets and tasks and note some interesting empirical observations.

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Haytham M. Fayek, Lawrence Cavedon, Hong Ren Wu. 2018-11-29. On the Transferability of Representations in Neural Networks Between Datasets and Tasks. https://arxiv.org/abs/1811.12273

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