Unsupervised Representation Learning to Aid Semi-Supervised Meta
  Learning
Atik Faysal Mohammad Rostami Huaxia Wang Avimanyu Sahoo Ryan Antle
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.