AutoDispNet: Improving Disparity Estimation With AutoML

Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, Thomas Brox; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 1812-1823

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.

Related Material


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[bibtex]
@InProceedings{Saikia_2019_ICCV,
author = {Saikia, Tonmoy and Marrakchi, Yassine and Zela, Arber and Hutter, Frank and Brox, Thomas},
title = {AutoDispNet: Improving Disparity Estimation With AutoML},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}