# From Weakly Supervised Learning to Biquality Learning: an Introduction

arXiv: https://arxiv.org/abs/2012.09632 · HTML: https://mlsanity.com/paper/2012.09632/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Novel Class Discovery: an Introduction and Key Concepts](https://mlsanity.com/paper/2302.12028/) (2023)
- [Robust Semi-Supervised Learning for Self-learning Open-World Classes](https://mlsanity.com/paper/2401.07551/) (2024)
- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [A Method for Discovering Novel Classes in Tabular Data](https://mlsanity.com/paper/2209.01217/) (2022)
- [Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation](https://mlsanity.com/paper/2301.05336/) (2023)
- [A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques](https://mlsanity.com/paper/2302.08571/) (2023)
- [Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark](https://mlsanity.com/paper/2410.15772/) (2024)
- [D'ecouvrir de nouvelles classes dans des donn'ees tabulaires](https://mlsanity.com/paper/2211.16352/) (2022)
- [biquality-learn: a Python library for Biquality Learning](https://mlsanity.com/paper/2308.09643/) (2023)
- [Biquality Learning: a Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts](https://mlsanity.com/paper/2308.15132/) (2023)

## Abstract (arXiv)

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.