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iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization.

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    This study introduces iDARTS, an improved method for Neural Architecture Search (NAS), to address instability issues in the original DARTS algorithm. iDARTS enhances robustness and generalization, achieving state-of-the-art results with significantly reduced computational cost.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Differentiable Architecture Search (DARTS) accelerates Neural Architecture Search (NAS) but suffers from instability and performance degradation with increased training epochs.
    • This degradation is attributed to imbalanced node norms and correlated operation outputs within the DARTS framework.

    Purpose of the Study:

    • To propose an improved DARTS algorithm, termed iDARTS, that enhances stability, robustness, and generalization in NAS.
    • To address the root causes of DARTS degradation: imbalanced node norms and operation output correlations.

    Main Methods:

    • iDARTS incorporates node normalization during the training phase to maintain balanced norms.
    • In the discretization phase, architecture approximation is based on output similarity between nodes and decorrelated operations, rather than parameter values.

    Main Results:

    • iDARTS achieved error rates of 2.25% on CIFAR-10 and 24.7% on ImageNet.
    • Architecture search was completed within 0.2 and 1.9 GPU-days, respectively, demonstrating significant efficiency.
    • iDARTS exhibited superior robustness and generalization compared to other DARTS-based methods.

    Conclusions:

    • The proposed iDARTS method effectively resolves the instability and degradation issues inherent in DARTS.
    • iDARTS offers a more robust and generalizable approach to Neural Architecture Search with improved computational efficiency.