An Empirical Study of Data Scale, Model Complexity, and Input Modalities in Visual Generalization
This study systematically investigates the mechanisms by which data scale, model complexity, and input modality influence the generalization performance of vision models. Within a unified experimental framework, the authors conduct controlled and large-scale ablation studies on synthetic functions and the CIFAR dataset, employing polynomial fitting, diverse CNN and Transformer architectures, and multimodal inputs—including RGB, grayscale, gradients, edges, and wavelet representations—to quantitatively compare the effects of these three core factors for the first time. The findings reveal that increasing training data consistently enhances generalization; greater model complexity yields non-monotonic improvements; removing color information substantially degrades performance; and the efficacy of explicit handcrafted priors is highly dependent on model architecture.