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AutoDispNet: Improving Disparity Estimation With AutoML
[article]
2019
arXiv
pre-print
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
arXiv:1905.07443v2
fatcat:ye7b4vvpynetnjyb46dovl6yqm