Scaling vision models by depth and parameter count does not consistently improve localisation-based explanation quality across architectures, datasets, and post-hoc methods; smaller models often perform comparably or better.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
CNN classifies nine magnetic states from visualized atomistic spin dynamics simulation images using EfficientNetV1B0.
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Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality
Scaling vision models by depth and parameter count does not consistently improve localisation-based explanation quality across architectures, datasets, and post-hoc methods; smaller models often perform comparably or better.
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CNN-Based Classifier for Automated Identification of Magnetic States in Spin Dynamics Simulations
CNN classifies nine magnetic states from visualized atomistic spin dynamics simulation images using EfficientNetV1B0.