Jovita Lukasik

Jovita Lukasik

PostDoc | Generation and Robustness in Neural Architecture Search

DARTS for Inverse Problems: a Study on Stability

Published in NeurIPS 2021 Deep Inverse Workshop, 2021

Jonas Geiping, Jovita Lukasik, Margret Keuper, Michael Moeller

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Abstract

Differentiable architecture search (DARTS) is a widely researched tool for neural architecture search, due to its promising results for image classification. The main benefit of DARTS is the effectiveness achieved through the weight-sharing one-shot paradigm, which allows efficient architecture search. In this work, we investigate DARTS in a systematic case study of inverse problems, which allows us to analyze these potential benefits in a controlled manner. Although we demonstrate that the success of DARTS can be extended from classification to reconstruction, our experiments yield a fundamental difficulty in the evaluation of DARTS-based methods: The results show a large variance in all test cases and the weight-sharing performance of the architecture found during training does not always reflect its final performance. We conclude the necessity to 1) report the results of any DARTS-based methods from several runs along with its underlying performance statistics and 2) show the correlation between the training and final architecture performance.

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Bibtex

  @inproceedings{
    geiping2021darts,
    title={DARTS for Inverse Problems: a Study on Stability},
    author={Jonas Geiping and Jovita Lukasik and Margret Keuper and Michael Moeller},
    booktitle={NeurIPS 2021 Workshop on Deep Learning and Inverse Problems},
    year={2021},
    url={https://openreview.net/forum?id=ty5XCitJfLA}
    }