TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning

Understand

Handling previously unseen tasks after given only a few training examples continues to be a tough challenge in machine learning.

• We propose TapNets, neural networks augmented with task-adaptive projection for improved few-shot learning.
• Here, employing a meta-learning strategy with episode-based training, a network and a set of per-class reference vectors are learned across widely varying tasks.
• At the same time, for every episode, features in the embedding space are linearly projected into a new space as a form of quick task-specific conditioning.