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

arXiv:2608.06896 · cs.LG · Submitted 2026-08-11 · Read on arXiv

Wei Wang, Gang Niu, Masashi Sugiyama

cs.LG

Submitted: 2026-08-11

Updated: 2026-08-12

Comments: Preprint of an invited review article. In Neural Networks, Handbook of Statistics, vol. 55, pp. 289-320, Elsevier, 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 20× in this paper · explained in 1 episode
• Loss Function — 19× in this paper · explained in 2 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Regularization — 9× in this paper · explained in 3 episodes
• Generalization — 7× in this paper · explained in 14 episodes
• Overfitting — 7× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 6 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Knowledge Distillation — 5× in this paper · explained in 15 episodes
• Adam — 4× in this paper · explained in 3 episodes
• Approximated Accuracy — 4× in this paper
• Oracle Accuracy — 4× in this paper
• Calibration — 3× in this paper · explained in 9 episodes
• Covering Rate — 3× in this paper
• Evaluation Framework — 3× in this paper · explained in 3 episodes
• Papers — 3× in this paper · explained in 3 episodes
• Theoretical Guarantees — 3× in this paper · explained in 2 episodes
• Few-Shot Learning — 2× in this paper · explained in 6 episodes
• Semi-supervised Learning — 2× in this paper · explained in 4 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Adversarial Robustness — 1× in this paper · explained in 4 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Contrastive Learning — 1× in this paper · explained in 9 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Generalization Capabilities — 1× in this paper · explained in 2 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Multilayer Perceptron (MLP) — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 3 episodes
• Sample Complexity — 1× in this paper · explained in 20 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semantic Segmentation — 1× in this paper · explained in 3 episodes
• Unsupervised Learning — 1× in this paper · explained in 2 episodes
• Weight Decay — 1× in this paper · explained in 2 episodes

Sources

• Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning
• Distilling the Knowledge in a Neural Network
• Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning

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