A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
-
Updated
Apr 7, 2025 - Python
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
😎 Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切:论文、代码、框架与库
[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features
🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库
Synthetic Minority Over-Sampling Technique for Regression
ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
[ICDE'20] ⚖️ A general, efficient ensemble framework for imbalanced classification. | 泛用,高效,鲁棒的类别不平衡学习框架
An implementation of the focal loss to be used with LightGBM for binary and multi-class classification problems
Parametric Contrastive Learning (ICCV2021) & GPaCo (TPAMI 2023)
Python-based implementations of algorithms for learning on imbalanced data.
Code repository for the online course Machine Learning with Imbalanced Data
[ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond
[NeurIPS’20] ⚖️ Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题
Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.
Papers about long-tailed tasks
A general, feasible, and extensible framework for classification tasks.
ResLT: Residual Learning for Long-tailed Recognition (TPAMI 2022)
A package for data science practitioners. This library implements a number of helpful, common data transformations with a scikit-learn friendly interface in an effort to expedite the modeling process.
Add a description, image, and links to the imbalanced-data topic page so that developers can more easily learn about it.
To associate your repository with the imbalanced-data topic, visit your repo's landing page and select "manage topics."