Author Archives: Arber Zela

AutoDispNet: Improving Disparity Estimation with AutoML

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Compared to the state of computer vision 20 years ago, deep learning has enabled more generic methodologies that can be applied to various tasks by automatically extracting meaningful features from the data. However, in practice those methodologies are not as generic as it looks at first glance. While standard neural networks may lead to reasonable solutions, results can be improved significantly by tweaking the details of this design: both the detailed architecture and several further hyperparameters which control the generalization properties of these networks to unseen data. Efficient AutoML in general and neural architecture search (NAS) in particular promise to relieve us from the manual tweaking effort by tuning hyperparameters / architectures to extract those features that maximize the generalization of neural networks. Motivated by the successes of AutoML and NAS for standard image recognition benchmarks, in our ICCV 2019 paper AutoDispNet: Improving Disparity Estimation with AutoML we set out to also apply them to encoder-decoder vision architectures. (more…)

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