Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions

Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data. However, this requirement is often not met in real-world applications. Weakly supervised learning aims to train an accurate model with incomplete, inexact, or inaccurate supervision. In this chapter, we will discuss recent advances in this field, including new supervision paradigms, relaxed assumptions, and practical solutions. First, we introduce a new weakly superv

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ethics.ai (7 August 2026), “Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions,” evidence record 18682, https://ethics.ai/record/18682 (originally published by arXiv fairness query).

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