Bounded Adjustment with Reliability-Guided Embedding for Imbalanced Learning with Noisy Labels

arXiv:2609.16380 · cs.LG, stat.ML · Submitted 2026-09-14 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 20 pages, 2 figures, and 9 tables

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

Terminology

• Consistency — 8× in this paper · explained in 3 episodes
• Calibration — 7× in this paper · explained in 7 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Bounded Adjustment with Reliability-Guided Embeddings — 3× in this paper
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Verification — 1× in this paper · explained in 2 episodes

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