Few-Shot Learning for Image Classification

In certain specialized domain scenarios, the collection of image data is rendered time-consuming and costly due to a variety of unique issues, making the assembly of extensive image datasets a challenging endeavor. When compared to the thousands, if not tens of thousands of image data required for deep learning models, the amount of image data that can be procured is significantly dwarfed. However, few-shot learning facilitates the training of models with a limited number of labeled images, eliminating the need for extensive data resources. In this paper, image enhancement activated to enrich the finite image dataset, thereby diversifying the data and highlighting key features through the improvement of visual effects and machine interpretability. Additionally, the method of transfer learning is engaged to optimize the classification performance of deep learning models. The selection of the ResNet-50 architecture serves as the foundational framework for the network. Prior knowledge is obtained from the ImageNet dataset. A strategy that combines image enhancement and transfer learning, where the original data is processed using image enhancement to improve the convergence rate of the loss function and the model's classification accuracy.