Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network

TL;DR

This paper introduces a meta-learning approach using order-equivariant networks for few-shot one-class classification, enabling effective predictions with minimal positive examples and no negative supervision, demonstrated on astronomical data.

Contribution

The paper proposes a novel meta-learning framework with order-equivariant networks for OCC, capable of generalizing to unseen tasks with limited positive data without retraining.

Findings

Outperforms baselines on synthetic streams without retraining

Shows promising results on real stellar stream data with fine-tuning

Highlights the importance of task similarity for transferability

Abstract

This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example. We consider that we have a set of `one-class classification' objective-tasks with only a small set of positive examples available for each task, and a set of training tasks with full supervision (i.e. highly imbalanced classification). We propose an approach using order-equivariant networks to learn a 'meta' binary-classifier. The model will take as input an example to classify from a given task, as well as the corresponding supervised set of positive examples for this OCC task. Thus, the output of the model will be 'conditioned' on the available positive example of a given task, allowing to predict on new tasks and new examples without labeled negative examples. In this paper, we are motivated by an astronomy application. Our goal is to identify if stars belong to a specific stellar group (the 'one-class' for a given task), called \textit{stellar streams}, where each stellar stream is a different OCC-task. We show that our method transfers well on unseen (test) synthetic streams, and outperforms the baselines even though it is not retrained and accesses a much smaller part of the data per task to predict (only positive supervision). We see however that it doesn't transfer as well on the real stream GD-1. This could come from intrinsic differences from the synthetic and real stream, highlighting the need for consistency in the 'nature' of the task for this method. However, light fine-tuning improve performances and outperform our baselines. Our experiments show encouraging results to further explore meta-learning methods for OCC tasks.