Local Propagation for Few-Shot Learning

01/05/2021
by   Yann Lifchitz, et al.
0

The challenge in few-shot learning is that available data is not enough to capture the underlying distribution. To mitigate this, two emerging directions are (a) using local image representations, essentially multiplying the amount of data by a constant factor, and (b) using more unlabeled data, for instance by transductive inference, jointly on a number of queries. In this work, we bring these two ideas together, introducing local propagation. We treat local image features as independent examples, we build a graph on them and we use it to propagate both the features themselves and the labels, known and unknown. Interestingly, since there is a number of features per image, even a single query gives rise to transductive inference. As a result, we provide a universally safe choice for few-shot inference under both non-transductive and transductive settings, improving accuracy over corresponding methods. This is in contrast to existing solutions, where one needs to choose the method depending on the quantity of available data.

READ FULL TEXT
research
03/31/2020

DPGN: Distribution Propagation Graph Network for Few-shot Learning

We extend this idea further to explicitly model the distribution-level r...
research
03/26/2020

Instance Credibility Inference for Few-Shot Learning

Few-shot learning (FSL) aims to recognize new objects with extremely lim...
research
12/14/2020

Iterative label cleaning for transductive and semi-supervised few-shot learning

Few-shot learning amounts to learning representations and acquiring know...
research
05/25/2018

Transductive Propagation Network for Few-shot Learning

Few-shot learning aims to build a learner that quickly generalizes to no...
research
06/07/2017

Low-shot learning with large-scale diffusion

This paper considers the problem of inferring image labels for which onl...
research
06/02/2021

One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations

The field of few-shot learning has made remarkable strides in developing...

Please sign up or login with your details

Forgot password? Click here to reset