Exploring representation learning for flexible few-shot tasks
Existing approaches to few-shot learning deal with tasks that have persistent, rigid notions of classes. Typically, the learner observes data only from a fixed number of classes at training time and is asked to generalize to a new set of classes at test time. Two examples from the same class would always be assigned the same labels in any episode. In this work, we consider a realistic setting where the relationship between examples can change from episode to episode depending on the task context, which is not given to the learner. We define two new benchmark datasets for this flexible few-shot scenario, where the tasks are based on images of faces (Celeb-A) and shoes (Zappos50K). While classification baselines learn representations that work well for standard few-shot learning, they suffer in our flexible tasks since the classification criteria shift from training to testing. On the other hand, unsupervised contrastive representation learning with instance-based invariance objectives preserves such flexibility. A combination of instance and class invariance learning objectives is found to perform best on our new flexible few-shot learning benchmarks, and a novel variant of Prototypical Networks is proposed for selecting useful feature dimensions.
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CelebA
