AINeutralarXiv – CS AI · 8h ago6/10
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An Empirical Study of Data Scale, Model Complexity, and Input Modalities in Visual Generalization
A research study empirically examines how data scale, model complexity, and input modalities affect visual generalization in deep neural networks using CIFAR-10/100 datasets. The findings reveal that increasing training data consistently improves generalization, while model complexity changes yield inconsistent results, and color information removal significantly degrades performance.