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 supervised binary classification problem called confidence-difference classification and propose consistent approaches to solve it. Next, we investigate complementary-label learning, a weakly supervised ...
| Authors | Wei Wang, Gang Niu, Masashi Sugiyama |
|---|---|
| Primary category | cs.LG |
| Published | 7 August 2026 |
| Last updated at source | 11 August 2026 |
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