Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data

AAAI Conference on Artificial Intelligence (AAAI), 2021

30 December 2021

Papers citing "Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data"

12 / 12 papers shown

LiLAW: Lightweight Learnable Adaptive Weighting to Meta-Learn Sample Difficulty, Improve Noisy Training, Increase Fairness, and Effectively Use Synthetic Data

279

25 Sep 2025

Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection

Yazhou Yao

244

17 Feb 2024

Understanding and Mitigating the Bias in Sample Selection for Learning with Noisy Labels

423

24 Jan 2024

MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic SegmentationIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023

284

22 Jan 2024

Geometric Prior Guided Feature Representation Learning for Long-Tailed ClassificationInternational Journal of Computer Vision (IJCV), 2024

Shuyuan Yang

249

21 Jan 2024

Data-Centric Long-Tailed Image Recognition

Yanbiao Ma

Licheng Jiao

Fang Liu

Shuyuan Yang

Xu Liu

Puhua Chen

400

03 Nov 2023

A Survey of Historical Learning: Learning Models with Learning History

Xiang Li

Lingfeng Yang

Jian Yang

296

23 Mar 2023

Dynamic Loss For Robust LearningIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022

378

22 Nov 2022

When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration MethodIEEE International Conference on Computer Vision (ICCV), 2022

409

20 Nov 2022

Learning from Long-Tailed Noisy Data with Sample Selection and Balanced LossInternational Joint Conference on Artificial Intelligence (IJCAI), 2022

303

20 Nov 2022

Label-Noise Learning with Intrinsically Long-Tailed DataIEEE International Conference on Computer Vision (ICCV), 2022

Bo Han

265

21 Aug 2022

CTRL: Clustering Training Losses for Label Error DetectionIEEE Transactions on Artificial Intelligence (IEEE TAI), 2022

C. Yue

N. Jha

NoLa

353

17 Aug 2022