An Empirical Study of Data Scale, Model Complexity, and Input Modalities in Visual Generalization
Modern deep neural networks usually have large parameter scales and nonlinear hierarchical structures, and they have achieved strong performance in computer vision. However, the source of their generalization performance remains difficult to explain using traditional statistical learning theory. Among the factors that may affect visual generalization, data scale, model complexity, and input modalities are fundamental and controllable variables. This study empirically analyzes how these three factors influence model generalization performance. Specifically, in a preliminary experiment, we construct a one-dimensional nonlinear function and vary the number of training samples and the polynom...
| Authors | Yidi Zhouluo |
|---|---|
| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 6 June 2026 |
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