Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Hao, Shah, Ankit, Wang, Jindong, Tao, Ran, Wang, Yidong, Xie, Xing, Sugiyama, Masashi, Singh, Rita, Raj, Bhiksha |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
by: Chen, Hao, et al.
Published: (2023)
by: Chen, Hao, et al.
Published: (2023)
Impact of Noisy Supervision in Foundation Model Learning
by: Chen, Hao, et al.
Published: (2024)
by: Chen, Hao, et al.
Published: (2024)
A General Framework for Learning from Weak Supervision
by: Chen, Hao, et al.
Published: (2024)
by: Chen, Hao, et al.
Published: (2024)
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
by: Chen, Hao, et al.
Published: (2022)
by: Chen, Hao, et al.
Published: (2022)
Weakly-Supervised Contrastive Learning for Imprecise Class Labels
by: Zhou, Zi-Hao, et al.
Published: (2025)
by: Zhou, Zi-Hao, et al.
Published: (2025)
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
by: Zhang, Jingfeng, et al.
Published: (2023)
by: Zhang, Jingfeng, et al.
Published: (2023)
Learning Robust Diffusion Models from Imprecise Supervision
by: Wu, Dong-Dong, et al.
Published: (2025)
by: Wu, Dong-Dong, et al.
Published: (2025)
Generative Motion Infilling From Imprecisely Timed Keyframes
by: Goel, Purvi, et al.
Published: (2025)
by: Goel, Purvi, et al.
Published: (2025)
Slight Corruption in Pre-training Data Makes Better Diffusion Models
by: Chen, Hao, et al.
Published: (2024)
by: Chen, Hao, et al.
Published: (2024)
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
by: Xie, Ming-Kun, et al.
Published: (2024)
by: Xie, Ming-Kun, et al.
Published: (2024)
XQ-GAN: An Open-source Image Tokenization Framework for Autoregressive Generation
by: Li, Xiang, et al.
Published: (2024)
by: Li, Xiang, et al.
Published: (2024)
Multi-Label Knowledge Distillation
by: Yang, Penghui, et al.
Published: (2023)
by: Yang, Penghui, et al.
Published: (2023)
Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes
by: Mai, Tan-Ha, et al.
Published: (2026)
by: Mai, Tan-Ha, et al.
Published: (2026)
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
by: Chen, Hao, et al.
Published: (2022)
by: Chen, Hao, et al.
Published: (2022)
Instance-dependent Label Distribution Estimation for Learning with Label Noise
by: Liao, Zehui, et al.
Published: (2022)
by: Liao, Zehui, et al.
Published: (2022)
Masked Autoencoders Are Effective Tokenizers for Diffusion Models
by: Chen, Hao, et al.
Published: (2025)
by: Chen, Hao, et al.
Published: (2025)
On The Robustness of Foundational 3D Medical Image Segmentation Models Against Imprecise Visual Prompts
by: Chattopadhyay, Soumitri, et al.
Published: (2026)
by: Chattopadhyay, Soumitri, et al.
Published: (2026)
Domain Generalisation via Imprecise Learning
by: Singh, Anurag, et al.
Published: (2024)
by: Singh, Anurag, et al.
Published: (2024)
Exploring Vacant Classes in Label-Skewed Federated Learning
by: Guo, Kuangpu, et al.
Published: (2024)
by: Guo, Kuangpu, et al.
Published: (2024)
QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition
by: Li, Xiang, et al.
Published: (2023)
by: Li, Xiang, et al.
Published: (2023)
Learning Semantic-Aware Threshold for Multi-Label Image Recognition with Partial Labels
by: Ruan, Haoxian, et al.
Published: (2025)
by: Ruan, Haoxian, et al.
Published: (2025)
Foster Adaptivity and Balance in Learning with Noisy Labels
by: Sheng, Mengmeng, et al.
Published: (2024)
by: Sheng, Mengmeng, et al.
Published: (2024)
Granular-ball Representation Learning for Deep CNN on Learning with Label Noise
by: Dai, Dawei, et al.
Published: (2024)
by: Dai, Dawei, et al.
Published: (2024)
Efficient Adaptive Label Refinement for Label Noise Learning
by: Zhang, Wenzhen, et al.
Published: (2025)
by: Zhang, Wenzhen, et al.
Published: (2025)
Label Sharing Incremental Learning Framework for Independent Multi-Label Segmentation Tasks
by: Anand, Deepa, et al.
Published: (2024)
by: Anand, Deepa, et al.
Published: (2024)
Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration
by: Chen, Tianshui, et al.
Published: (2024)
by: Chen, Tianshui, et al.
Published: (2024)
ControlVAR: Exploring Controllable Visual Autoregressive Modeling
by: Li, Xiang, et al.
Published: (2024)
by: Li, Xiang, et al.
Published: (2024)
Multimodal Label Relevance Ranking via Reinforcement Learning
by: Guo, Taian, et al.
Published: (2024)
by: Guo, Taian, et al.
Published: (2024)
$\text{R}^2$-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
by: Li, Xiang, et al.
Published: (2024)
by: Li, Xiang, et al.
Published: (2024)
Rebalancing Multi-Label Class-Incremental Learning
by: Du, Kaile, et al.
Published: (2024)
by: Du, Kaile, et al.
Published: (2024)
Text-Region Matching for Multi-Label Image Recognition with Missing Labels
by: Ma, Leilei, et al.
Published: (2024)
by: Ma, Leilei, et al.
Published: (2024)
Self-Ensemble Post Learning for Noisy Domain Generalization
by: Lu, Wang, et al.
Published: (2025)
by: Lu, Wang, et al.
Published: (2025)
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution
by: Chen, Ning, et al.
Published: (2025)
by: Chen, Ning, et al.
Published: (2025)
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
by: Zhang, Jifan, et al.
Published: (2023)
by: Zhang, Jifan, et al.
Published: (2023)
Completing Visual Objects via Bridging Generation and Segmentation
by: Li, Xiang, et al.
Published: (2023)
by: Li, Xiang, et al.
Published: (2023)
Weak-to-Strong Diffusion with Reflection
by: Bai, Lichen, et al.
Published: (2025)
by: Bai, Lichen, et al.
Published: (2025)
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
by: Zhou, Yuyin, et al.
Published: (2022)
by: Zhou, Yuyin, et al.
Published: (2022)
Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning
by: Ma, LeiLei, et al.
Published: (2025)
by: Ma, LeiLei, et al.
Published: (2025)
Love Me, Love My Label: Rethinking the Role of Labels in Prompt Retrieval for Visual In-Context Learning
by: Luo, Tianci, et al.
Published: (2026)
by: Luo, Tianci, et al.
Published: (2026)
CRoF: CLIP-based Robust Few-shot Learning on Noisy Labels
by: Deng, Shizhuo, et al.
Published: (2024)
by: Deng, Shizhuo, et al.
Published: (2024)
Similar Items
-
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
by: Chen, Hao, et al.
Published: (2023) -
Impact of Noisy Supervision in Foundation Model Learning
by: Chen, Hao, et al.
Published: (2024) -
A General Framework for Learning from Weak Supervision
by: Chen, Hao, et al.
Published: (2024) -
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
by: Chen, Hao, et al.
Published: (2022) -
Weakly-Supervised Contrastive Learning for Imprecise Class Labels
by: Zhou, Zi-Hao, et al.
Published: (2025)