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arXiv · 1810.05466

Mode Normalization

Lucas Deecke·Iain Murray·Hakan Bilen
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Abstract

Normalization methods are a central building block in the deep learning toolbox. They accelerate and stabilize training, while decreasing the dependence on manually tuned learning rate schedules. When learning from multi-modal distributions, the effectiveness of batch normalization (BN), arguably the most prominent normalization method, is reduced. As a remedy, we propose a more flexible approach: by extending the normalization to more than a single mean and variance, we detect modes of data on-the-fly, jointly normalizing samples that share common features. We demonstrate that our method outperforms BN and other widely used normalization techniques in several experiments, including single and multi-task datasets.

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Lucas Deecke, Iain Murray, Hakan Bilen. 2018-10-12. Mode Normalization. https://arxiv.org/abs/1810.05466

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