Powering Neural Architecture Search with Robust Masked Autoencoders
Keywords: Neural Architecture Search, Masked Autoencoder
TL;DR: A novel unsupervised method for neural architecture search.
Abstract: Neural Architecture Search (NAS) relies heavily on labeled data, which is labor-intensive and time-consuming to obtain. In this paper, we propose a novel NAS method based on an unsupervised paradigm, specifically Masked Autoencoders (MAE), thereby eliminating the need for labeled data during the searching process. By replacing the supervised learning objective with an image reconstruction task, our approach enables the robust discovery of network architectures without compromising performance and generalization ability. Additionally, we address the problem of performance collapse encountered in the widely-used Differentiable Architecture Search (DARTS) in the unsupervised setting by designing a hierarchical decoder. Through extensive experiments conducted across various search spaces and datasets, we demonstrate the effectiveness and robustness of our method, offering empirical evidence of its superiority over baseline approaches.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 7684
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