Evaluating the Robustness of Transfer Learning with Recipes on Small Data– Using Data of Birds as an Example

Abstract

Processing small data in machine learning often leads to challenges like low accuracy and overfitting. To address these issues effectively, it is essential to assess the integrity of the underlying problem. One effective approach to tackling such challenges is to adopt a top-down strategy, focusing on adjusting and creating a suitable framework. In this paper, various techniques will be employed to fine-tune the model for optimization. Experiments will be conducted on six distinct datasets to enhance the model’s accuracy and prevent overfitting.