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Self-supervised pairwise-sample resistance model for few-shot classification
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AI
DOI：10.1007/s10489-023-04525-4.png)
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摘要
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The traditional supervised learning models rely on high-quality labeled samples heavily. In many fields, training the model on limited labeled samples will result in a weak generalization ability of the model. To address this problem, we propose a novel few-shot image classification method by self-supervised and metric learning, which contains two training steps: (1) Training the feature extractor and projection head with strong representational ability by self-supervised technology; (2) taking the trained feature extractor and projection head as the initialization meta-learning model, and fine-tuning the meta-learning model by the proposed loss functions. Specifically, we construct the pairwise-sample meta loss (ML) to consider the influence of each sample on the target sample in the feature space, and propose a novel regularization technique named resistance regularization based on pairwise-samples which is utilized as an auxiliary loss in the meta-learning model. The model performance is evaluated on the 5-way 1-shot and 5-way 5-shot classification tasks of mini-ImageNet and tired-ImageNet. The results demonstrate that the proposed method achieves the state-of-the-art performance.
Keyword:
Few-shot
Self-supervised
Meta-learning
Pairwise-sample
Regularization
期刊
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3.5
论文数:
7.6K
被引数:
1.7W
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