Few-shot image classification based on transfer learning and data enhancement

The task of few-shot image classification involves dividing a large number of unknown label samples using only a limited number of known label samples. Previous approaches commonly incorporated an efficient initial embedding network into the meta-learning process, which significantly influenced model performance. However, these methods fail to address the issue of overfitting in few-shot learning (FSL) due to the scarcity of available data. Traditional convolutional neural networks also struggle to effectively extract information from such limited samples. Therefore, this paper proposes the introduction of AmdimNet as an embedded network that maximizes mutual information among samples, enabling it to capture detailed feature information from each individual sample within this constrained setting. Additionally, we perform simple data augmentation on the support set to increase its size and mitigate overfitting occurrences. Finally, we adopt a suitable evaluation metric to enhance classification accuracy. Experimental results demonstrate significant improvements achieved by our proposed method on mainstream few-shot image classification benchmark datasets.