Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning
This work addresses the challenge of requiring large labeled datasets for meta-learning, offering a model-agnostic approach to enhance accuracy in few-shot learning scenarios.
The paper tackles the data scarcity problem in few-shot learning by proposing a one-shot unsupervised meta-learning method to learn latent representations, which are then used to initialize supervised meta-learning models, achieving improved accuracy with fewer training samples on Omniglot and mini-Imagenet datasets.
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.