Improving Neural Architecture Search by Minimizing Worst-Case Validation Loss

Neural architecture search (NAS) aims at automatically searching for high-performance architectures and has achieved considerable progress. Existing NAS methods learn architectures by minimizing average-case validation losses. As a result, the searched architectures are less capable of making correct predictions under worst-case scenarios. To address this problem, we propose a framework which leverages a deep generative model to generate adversarial validation examples to measure the worst-case validation performance of an architecture and improves the architecture by minimizing the loss on the generated adversarial validation data. Our framework is based on multi-level optimization, which performs multiple learning stages end-to-end. Experiments on a variety of datasets demonstrate the effectiveness of our method.