Discriminative k-shot learning using probabilistic models

Understand

This paper introduces a probabilistic framework for k-shot image classification.

• The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples.
• The new approach not only leverages the feature-based representation learned by a neural network from the initial task (representational transfer), but also information about the classes (concept transfer).
• The concept information is encapsulated in a probabilistic model for the final layer weights of the neural network which acts as a prior for probabilistic k-shot learning.