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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Differentiable Architecture Search (DARTS) offers effective neural architecture search (NAS) via gradient descent.
    • DARTS faces challenges with high memory and computational demands.

    Purpose of the Study:

    • Introduce Partially-Connected DARTS (PC-DARTS) for efficient and stable neural architecture search.
    • Reduce channel and spatial redundancies in the super-network to improve search efficiency.

    Main Methods:

    • Implement partial channel connection by sampling a subset of channels for operation selection.
    • Introduce side operations to bypass non-sampled channels, ensuring performance.
    • Apply spatial down-sampling to eliminate redundancy and enhance mixed computation efficiency.
    • Develop edge normalization to align edge selection with channel sampling and architectural parameters.

    Main Results:

    • PC-DARTS demonstrates improved search speed and training stability compared to standard DARTS.
    • Achieved a 2.55% top-1 error rate on CIFAR-10 with only 0.07 GPU-days.
    • Attained a state-of-the-art 24.1% top-1 error rate on ImageNet (mobile setting) within 2.8 GPU-days.

    Conclusions:

    • PC-DARTS offers a more efficient and stable approach to neural architecture search.
    • The proposed methods effectively reduce computational overhead while maintaining high performance.