Cover Image

HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Peng, Ningkang, Mao, Jingyang, Yu, Qianfeng, Peng, Xiaoqian, Ma, Peirong, Gu, Yanhui
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GAMR: Geometric-Aware Manifold Regularization with Virtual Outlier Synthesis for Learning with Noisy Labels
by: Peng, Ningkang, et al.
Published: (2026)

When Accuracy Is Not Enough: Uncertainty Collapse between Noisy Label Learning and Out-of-Distribution Detection
by: Peng, Ningkang, et al.
Published: (2026)

Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning
by: Peng, Ningkang, et al.
Published: (2026)

Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label
by: Mao, Jingyang, et al.
Published: (2026)

VMF-GOS: Geometry-guided virtual Outlier Synthesis for Long-Tailed OOD Detection
by: Peng, Ningkang, et al.
Published: (2026)

Breaking Semantic Hegemony: Decoupling Principal and Residual Subspaces for Generalized OOD Detection
by: Peng, Ningkang, et al.
Published: (2026)

PIS: A Physics-Informed System for Accurate State Partitioning of $Aβ_{42}$ Protein Trajectories
by: Yu, Qianfeng, et al.
Published: (2026)

Don't Break the Boundary: Continual Unlearning for OOD Detection Based on Free Energy Repulsion
by: Peng, Ningkang, et al.
Published: (2026)

Learning with Adaptive Prototype Manifolds for Out-of-Distribution Detection
by: Peng, Ningkang, et al.
Published: (2026)

Early High-Frequency Injection for Geometry-Sensitive OOD Detection
by: Cheng, Chuanjie, et al.
Published: (2026)

Halt the Hallucination: Decoupling Signal and Semantic OOD Detection Based on Cascaded Early Rejection
by: Peng, Ningkang, et al.
Published: (2026)

Is Complex Training Necessary for Long-Tailed OOD Detection? A Re-think from Feature Geometry
by: Peng, Ningkang, et al.
Published: (2026)

How to Achieve Prototypical Birth and Death for OOD Detection?
by: Peng, Ningkang, et al.
Published: (2026)

A Multi-dimensional Semantic Surprise Framework Based on Low-Entropy Semantic Manifolds for Fine-Grained Out-of-Distribution Detection
by: Peng, Ningkang, et al.
Published: (2025)

DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks
by: Zhang, Jiaxin, et al.
Published: (2024)

Data Stream Sampling with Fuzzy Task Boundaries and Noisy Labels
by: Chen, Yu-Hsi
Published: (2024)

Affordance-First Decomposition for Continual Learning in Video-Language Understanding
by: Xu, Mengzhu, et al.
Published: (2025)

HamVision: Hamiltonian Dynamics as Inductive Bias for Medical Image Analysis
by: Mabrok, Mohamed A
Published: (2026)

Semantic-Consistent Bidirectional Contrastive Hashing for Noisy Multi-Label Cross-Modal Retrieval
by: Peng, Likang, et al.
Published: (2025)

MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
by: Hu, Chuanyang, et al.
Published: (2023)

Skeleton-Based Human Action Recognition with Noisy Labels
by: Xu, Yi, et al.
Published: (2024)

Noisy Label Refinement with Semantically Reliable Synthetic Images
by: Li, Yingxuan, et al.
Published: (2025)

Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
by: Zhao, Rui, et al.
Published: (2024)

Active Negative Loss: A Robust Framework for Learning with Noisy Labels
by: Ye, Xichen, et al.
Published: (2024)

Noisy-Correspondence Learning for Text-to-Image Person Re-identification
by: Qin, Yang, et al.
Published: (2023)

Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels
by: Pu, Ruitao, et al.
Published: (2025)

Exploring Video-Based Driver Activity Recognition under Noisy Labels
by: Fan, Linjuan, et al.
Published: (2025)

ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memory
by: Wang, Jianchao, et al.
Published: (2025)

FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels
by: Li, Jichang, et al.
Published: (2023)

Combating Noisy Labels via Dynamic Connection Masking
by: Zhang, Xinlei, et al.
Published: (2025)

Active Learning with a Noisy Annotator
by: Shafir, Netta, et al.
Published: (2025)

Federated Learning with Instance-Dependent Noisy Label
by: Wang, Lei, et al.
Published: (2023)

Joint Asymmetric Loss for Learning with Noisy Labels
by: Wang, Jialiang, et al.
Published: (2025)

Foster Adaptivity and Balance in Learning with Noisy Labels
by: Sheng, Mengmeng, et al.
Published: (2024)

Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations
by: Kim, Heewon, et al.
Published: (2024)

Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning
by: Saravanan, Darshana, et al.
Published: (2024)

Online Writer Retrieval with Chinese Handwritten Phrases: A Synergistic Temporal-Frequency Representation Learning Approach
by: Zhang, Peirong, et al.
Published: (2024)

Effective Decision Boundary Learning for Class Incremental Learning
by: Li, Kunchi, et al.
Published: (2023)

PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation Learning
by: Liu, Xiaoyu, et al.
Published: (2024)

Probabilistic Machine Learning for Noisy Labels in Earth Observation
by: Kondylatos, Spyros, et al.
Published: (2025)