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arXiv · 1905.07443

AutoDispNet: Improving Disparity Estimation With AutoML

Tonmoy Saikia·Yassine Marrakchi·Arber Zela·Frank Hutter·Thomas Brox
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Abstract

Much research work in computer vision is being spent on optimizing existing network architectures to obtain a few more percentage points on benchmarks. Recent AutoML approaches promise to relieve us from this effort. However, they are mainly designed for comparatively small-scale classification tasks. In this work, we show how to use and extend existing AutoML techniques to efficiently optimize large-scale U-Net-like encoder-decoder architectures. In particular, we leverage gradient-based neural architecture search and Bayesian optimization for hyperparameter search. The resulting optimization does not require a large-scale compute cluster. We show results on disparity estimation that clearly outperform the manually optimized baseline and reach state-of-the-art performance.

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Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, Thomas Brox. 2019-05-17. AutoDispNet: Improving Disparity Estimation With AutoML. https://arxiv.org/abs/1905.07443

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