🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
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Updated
Dec 23, 2022 - Python
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
😎 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
Code repository for the online course Machine Learning with Imbalanced Data
A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features
[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.
A curated list of long-tailed recognition resources.
🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库
[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
[CVPR 2022 Oral] Balanced MSE for Imbalanced Visual Regression https://arxiv.org/abs/2203.16427
Parametric Contrastive Learning (ICCV2021) & GPaCo (TPAMI 2023)
Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.
[NeurIPS 2020] Balanced Meta-Softmax for Long-Tailed Visual Recognition
[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
Implementation of the Geometric SMOTE over-sampling algorithm.
[ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond
Implement GANs to generate time-series signals for imbalanced learning problem. The experiments are conducted using CWRU bearing data.
The objective of this work is to develop machine learning (ML) methods that can accurately predict adverse drug reactions (ADRs) using the databases SIDER and OFFSIDES.
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