A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
翻译 - （PyTorch）不平衡数据集采样器，用于对低频率类进行过采样和对高频率类进行欠采样。
#Awesome#😎 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
#自然语言处理#[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
#计算机科学#🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库
Synthetic Minority Over-Sampling Technique for Regression
#自然语言处理#ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
#计算机科学#[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)
#计算机科学#Python-based implementations of algorithms for learning on imbalanced data.
#计算机科学#Code repository for the online course Machine Learning with Imbalanced Data
#计算机科学#[ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond
#计算机科学#Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.
[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
A general, feasible, and extensible framework for classification tasks.
ResLT: Residual Learning for Long-tailed Recognition (TPAMI 2022)
datascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries