Class-wise Attention Reinforcement for Semi-supervised Meta-Learning
Xiaohang Pan, Fanzhang Li · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
Meta-learning aims to learn some common knowledge quickly from the limited labeled examples. Semi-supervised meta-learning is developed to improve the learning performance of the learner using limited labeled data and available unlabeled data. This paper proposes a new semi-supervised meta-learning method called the class-wise attention reinforcement (CWAR) method from the distinction of class representation and the credibility of pseudo-labeling. Firstly, we propose a class-wise attention (CWA) module to generate the class-wise attention weight vectors and apply them to prototype, query set, and unlabeled auxiliary set, reinforcing the attention on critical features in the task. Then, we use the random data augmentation method to assign pseudo labels to unlabeled data. Moreover, to mitigate the effects of misclassification and noise-influenced samples on the prototype, generating weight as the coefficient to calculate the new prototype. Experimental results on two popular benchmarks demonstrate the proposed method’s effectiveness and have highly competitive performance compared with the state-of-the-art.