Neural Architecture Search via Bregman Iterations

• URL: http://arxiv.org/abs/2106.02479v1
• Date: Fri, 4 Jun 2021 13:37:47 GMT
• Title: Neural Architecture Search via Bregman Iterations
• Authors: Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger
• Abstract summary: We propose a novel strategy for Neural Architecture Search (NAS) based on Bregman iterations. Our gradient-based one-shot algorithm gradually adds relevant parameters in an inverse scale space manner.
• Score: 1.7778609937758323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel strategy for Neural Architecture Search (NAS) based on Bregman iterations. Starting from a sparse neural network our gradient-based one-shot algorithm gradually adds relevant parameters in an inverse scale space manner. This allows the network to choose the best architecture in the search space which makes it well-designed for a given task, e.g., by adding neurons or skip connections. We demonstrate that using our approach one can unveil, for instance, residual autoencoders for denoising, deblurring, and classification tasks. Code is available at https://github.com/TimRoith/BregmanLearning.

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