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New SWAP-Score metric evaluates neural networks without training

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SWAP-Score, a novel zero-shot metric designed to evaluate neural networks without requiring training. This method measures a network's expressivity using sample-wise activation patterns and demonstrates strong predictive performance across various architectures, including CNNs and Transformers. SWAP-Score significantly outperforms existing metrics in computer vision and natural language processing tasks, showing high correlations with ground-truth performance and enabling faster neural architecture search. AI

IMPACT Enables faster and more accurate neural architecture search by reducing computational overhead in model evaluation.

RANK_REASON The cluster contains an academic paper introducing a new method for evaluating neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SWAP-Score metric evaluates neural networks without training

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The cluster contains an academic paper introducing a new method for evaluating neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaojun Chang ·

    Zero-Shot Neural Network Evaluation with Sample-Wise Activation Patterns

    Zero-shot proxies, also known as training-free metrics, are widely adopted to reduce the computational overhead in neural network evaluation for scenarios such as Neural Architecture Search (NAS), as they do not require any training. Existing zero-shot metrics have several limita…

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