Discriminative k-shot learning using probabilistic models

06/01/2017
by   Matthias Bauer, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/12/2018

Metalearning with Hebbian Fast Weights

We unify recent neural approaches to one-shot learning with older ideas ...
07/08/2018

Large Margin Few-Shot Learning

The key issue of few-shot learning is learning to generalize. In this pa...
04/28/2022

It's DONE: Direct ONE-shot learning without training optimization

Learning a new concept from one example is a superior function of human ...
06/07/2017

Low-shot learning with large-scale diffusion

This paper considers the problem of inferring image labels for which onl...
05/03/2019

Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning

Given an initial recognition model already trained on a set of base clas...
06/20/2018

Uncertainty in Multitask Transfer Learning

Using variational Bayes neural networks, we develop an algorithm capable...
12/30/2021

On the Role of Neural Collapse in Transfer Learning

We study the ability of foundation models to learn representations for c...