Towards Bridging Sample Complexity and Model Capacity
Keywords:Computer Vision (CV)
AbstractIn this paper, we give a new definition for sample complexity, and further develop a theoretical analysis to bridge the gap between sample complexity and model capacity. In contrast to previous works which study on some toy samples, we conduct our analysis on more general data space, and build a qualitative relationship from sample complexity to model capacity required to achieve comparable performance. Besides, we introduce a simple indicator to evaluate the sample complexity based on continuous mapping. Moreover, we further analysis the relationship between sample complexity and data distribution, which paves the way to understand the present representation learning. Extensive experiments on several datasets well demonstrate the effectiveness of our evaluation method.
How to Cite
Mei, S., Zhao, C., Yuan, S., & Ni, B. (2022). Towards Bridging Sample Complexity and Model Capacity. Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 1972-1980. https://doi.org/10.1609/aaai.v36i2.20092
AAAI Technical Track on Computer Vision II