META-LEARNING WITH DIVERSE TASKS

Abstract

Meta-learning models are improved for few-shot learning of unseen tasks by improving task diversity of training data used for training the meta-learning model. A task diversity score may be determined between a pair of tasks that partition a domain into respective classes. The respective classes are paired and scored to determine similarity between class pairs and subsequent task diversity scores. Diverse tasks may be generated with unsupervised analysis of the domain by determining disentangled latent features of the data samples. Each latent feature may then be considered a task with classes based on a clustering of the data samples based on the feature values of the respective latent feature. The classes are then used as training task labels for the data samples and sampled from to generate diverse tasks for the meta-learning model.

CPC Classifications