Abstract
ProtoPNet proposed by Chen et al. is able to provide interpretability that conforms to human intuition, but it requires many iterations of training to learn class-specific prototypes and does not support few-shot learning. We propose the few-shot learning version of ProtoPNet by using MAML, enabling it to converge quickly on different classification tasks. We test our model on the Omniglot and MiniImagenet datasets and evaluate their prototype interpretability. Our experiments show that MAML-ProtoPNet is a transparent model that can achieve or even exceed the baseline accuracy, and its prototype can learn class-specific features, which are consistent with our human recognition.
Yue Wang is the corresponding author of this paper (yuelwang@163.com). Yapu Zhao proposes the idea of combing MAML and ProtoPNet. And Yue Wang proposes the consistency of features (CoF) indicator in this paper. This work is supported by: National Defense Science and Technology Innovation Special Zone Project (No. 18-163-11-ZT-002-045-04); Engineering Research Center of State Financial Security, Ministry of Education, Central University of Finance and Economics, Beijing, 102206, China; Program for Innovation Research in Central University of Finance and Economics; National College Students’ Innovation and Entrepreneurship Training Program “Research and development of interpretable algorithms and prototype system for small sample image recognition”.
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Zhao, Y., Wang, Y., Zhai, X. (2022). Preliminary Study on Adapting ProtoPNet to Few-Shot Learning Using MAML. In: Wang, Y., Zhu, G., Han, Q., Wang, H., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1628. Springer, Singapore. https://doi.org/10.1007/978-981-19-5194-7_11
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