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Life sciences · Preprint
arXiv · August 7, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed preprint describing theoretical advances in weakly supervised learning methods, including confidence-difference classification, complementary-label learning, and partial-label learning frameworks. The abstract does not report empirical validation, clinical outcomes, or comparative performance metrics, and has not undergone peer review.
Preprint.
This is an unrefereed preprint describing theoretical advances in weakly supervised learning methods, including confidence-difference classification, complementary-label learning, and partial-label learning frameworks. The abstract does not report empirical validation, clinical outcomes, or comparative performance metrics, and has not undergone peer review.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is an unrefereed arXiv preprint presenting methodological advances in machine learning theory without peer review or clinical validation.
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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 multi-class classification problem. Our proposed approaches are based on more relaxed assumptions about the data generation process than existing consistent approaches. Lastly, we present an evaluation framework for partial-label learning, another popular multi-class weakly supervised learning problem, in order to promote fair and realistic evaluation of algorithms in this field.
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