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FX-DARTS: Designing Topology-Unconstrained Architectures With Differentiable Architecture Search and

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    This study introduces flexible DARTS (FX-DARTS), a method that removes architectural constraints in differentiable architecture search. FX-DARTS enables the discovery of novel neural network architectures with improved flexibility and performance.

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    Area of Science:

    • Artificial Intelligence
    • Computer Science

    Background:

    • Differentiable Architecture Search (DARTS) imposes strong priors, limiting architectural flexibility and hindering automated machine learning (AutoML) advancements.
    • Existing priors, while aiding optimization, restrict the exploration of more powerful neural network architectures.

    Purpose of the Study:

    • To reduce prior constraints in DARTS by eliminating restrictions on cell topology and modifying super-network discretization.
    • To introduce a flexible DARTS (FX-DARTS) method that maintains search stability within an enlarged search space.

    Main Methods:

    • Developed the flexible DARTS (FX-DARTS) method.
    • Leveraged an entropy-based super-network shrinking (ESS) framework to manage the enlarged search space.
    • Eliminated strict prior rules on cell topology and modified discretization mechanisms.

    Main Results:

    • FX-DARTS successfully derives neural architectures without rigid prior rules.
    • The method maintains search stability despite increased architectural flexibility.
    • Experimental results on image classification benchmarks show competitive performance-complexity trade-offs.

    Conclusions:

    • FX-DARTS enhances architectural flexibility in differentiable architecture search.
    • The approach allows for the discovery of novel, high-performing neural network architectures.
    • This method offers a promising direction for future AutoML research.