Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification

• URL: http://arxiv.org/abs/2411.12151v1
• Date: Tue, 19 Nov 2024 01:01:56 GMT
• Title: Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification
• Authors: Yuyang Xiao,
• Abstract summary: We first pre-train the model with self-supervision to enable it to learn common feature expressions on a large amount of unlabeled data. Then fine-tune it on the few-shot dataset Mini-ImageNet to improve the model's accuracy and generalization ability under limited data.
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• Abstract: This study aims to optimize the few-shot image classification task and improve the model's feature extraction and classification performance by combining self-supervised learning with the deep network model ResNet-101. During the training process, we first pre-train the model with self-supervision to enable it to learn common feature expressions on a large amount of unlabeled data; then fine-tune it on the few-shot dataset Mini-ImageNet to improve the model's accuracy and generalization ability under limited data. The experimental results show that compared with traditional convolutional neural networks, ResNet-50, DenseNet, and other models, our method has achieved excellent performance of about 95.12% in classification accuracy (ACC) and F1 score, verifying the effectiveness of self-supervised learning in few-shot classification. This method provides an efficient and reliable solution for the field of few-shot image classification.

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