Key Factors Determining the Required Number of Training Images in Person Re-Identification

Abstract

Focusing on person re-identification datasets, this paper proposes a new method to estimate the test accuracy curve over the training image number in a precise, interpretable, and efficient manner to receive financial and privacy protection benefits. An existing method, neural scaling law, accurately approximates the curve by fitting a regression function to data points of a training image number and the corresponding accuracy. However, fitting such a function does not explain the reason for the estimated curve. Moreover, obtaining a data point updates model parameters with heavy computation. Therefore, this paper investigates the key factors of a person re-identification dataset that determine the regression parameters. By incorporating the found factors, our method becomes interpretable. Simultaneously, the method significantly reduces computation costs since model updates are no longer needed. We experimentally show that our method is as precise as the uninterpretable neural scaling law incurring nearly millions of model updates.

Keywords

• Efficiency
• interpretability
• neural scaling laws