Discriminative k-shot learning using probabilistic models

Matthias Bauer; Mateo Rojas-Carulla; Jakub Bartłomiej Świątkowski; Bernhard Schölkopf; Richard E. Turner

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arXiv · 1706.00326

Discriminative k-shot learning using probabilistic models

Matthias Bauer·Mateo Rojas-Carulla·Jakub Bartłomiej Świątkowski·Bernhard Schölkopf·Richard E. Turner

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Abstract

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. We show that even a simple probabilistic model achieves state-of-the-art on a standard k-shot learning dataset by a large margin. Moreover, it is able to accurately model uncertainty, leading to well calibrated classifiers, and is easily extensible and flexible, unlike many recent approaches to k-shot learning.

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Matthias Bauer, Mateo Rojas-Carulla, Jakub Bartłomiej Świątkowski, Bernhard Schölkopf, Richard E. Turner. 2017-12-09. Discriminative k-shot learning using probabilistic models. https://arxiv.org/abs/1706.00326

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