Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning

• URL: http://arxiv.org/abs/2310.13085v1
• Date: Thu, 19 Oct 2023 18:25:22 GMT
• Title: Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning
• Authors: Atik Faysal, Mohammad Rostami, Huaxia Wang, Avimanyu Sahoo, and Ryan Antle
• Abstract summary: We propose a one-shot unsupervised meta-learning to learn latent representation of training samples. A temperature-scaled cross-entropy loss is used in the inner loop of meta-learning to prevent overfitting. The proposed method is model agnostic and can aid any meta-learning model to improve accuracy.
• Score: 16.534014215010757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot learning or meta-learning leverages the data scarcity problem in machine learning. Traditionally, training data requires a multitude of samples and labeling for supervised learning. To address this issue, we propose a one-shot unsupervised meta-learning to learn the latent representation of the training samples. We use augmented samples as the query set during the training phase of the unsupervised meta-learning. A temperature-scaled cross-entropy loss is used in the inner loop of meta-learning to prevent overfitting during unsupervised learning. The learned parameters from this step are applied to the targeted supervised meta-learning in a transfer-learning fashion for initialization and fast adaptation with improved accuracy. The proposed method is model agnostic and can aid any meta-learning model to improve accuracy. We use model agnostic meta-learning (MAML) and relation network (RN) on Omniglot and mini-Imagenet datasets to demonstrate the performance of the proposed method. Furthermore, a meta-learning model with the proposed initialization can achieve satisfactory accuracy with significantly fewer training samples.

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