From Weakly Supervised Learning to Biquality Learning: an Introduction

TL;DR

This paper introduces Biquality Learning as a unified framework for Weakly Supervised Learning, providing a structured overview of the field and proposing measurable quantities to characterize different supervision scenarios.

Contribution

It offers a comprehensive review of WSL, introduces the WSL cube framework with measurable coordinates, and unifies various WSL approaches under the Biquality Learning paradigm.

Findings

Proposes the WSL cube with Quality, Adaptability, and Quantity as coordinates.

Defines Biquality Learning as a unified framework for WSL.

Provides an overview and categorization of WSL research.

Abstract

The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies". In WSL use cases, a variety of situations exists where the collected "information" is imperfect. The paradigm of WSL attempts to list and cover these problems with associated solutions. In this paper, we review the research progress on WSL with the aim to make it as a brief introduction to this field. We present the three axis of WSL cube and an overview of most of all the elements of their facets. We propose three measurable quantities that acts as coordinates in the previously defined cube namely: Quality, Adaptability and Quantity of information. Thus we suggest that Biquality Learning framework can be defined as a plan of the WSL cube and propose to re-discover previously unrelated patches in WSL literature as a unified Biquality Learning literature.