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DARTS without a Validation Set: Optimizing the Marginal Likelihood
Miroslav Fil · Robin Ru · Clare Lyle · Yarin Gal
Event URL: https://openreview.net/forum?id=661Wz3zOzlt »

The success of neural architecture search (NAS) has historically been limited by excessive compute requirements. While modern weight-sharing NAS methods such as DARTS are able to finish the search in single-digit GPU days, extracting the final best architecture from the shared weights is notoriously unreliable. Training-Speed-Estimate (TSE), a recently developed generalization estimator with a Bayesian marginal likelihood interpretation, has previously been used in place of the validation loss for gradient-based optimization in DARTS. This prevents the DARTS skip connection collapse, which significantly improves performance on NASBench-201 and the original DARTS search space. We extend those results by applying various DARTS diagnostics and show several unusual behaviors arising from not using a validation set. Furthermore, our experiments yield concrete examples of the depth gap and topology selection in DARTS having a strongly negative impact on the search performance despite generally receiving limited attention in the literature compared to the operations selection.

Author Information

Miroslav Fil (University of Oxford)
Robin Ru (Oxford University)
Clare Lyle (University of Oxford)
Yarin Gal (University of Oxford)

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