Popular GitHub Repositories for Imbalanced Classes

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

??High-Performance Face Recognition Library on PaddlePaddle & PyTorch??

[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression

Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN (GRU and LSTM) and Word Embeddings on Tensorflow.

Anno-Mage: A Semi Automatic Image Annotation Tool which helps you in annotating images by suggesting you annotations for 80 object classes using a pre-trained model

MemoRizz: A Python library serving as a memory layer for AI applications. Leverages popular databases and storage solutions to optimize memory usage. Provides utility classes and methods for efficient data management, including MongoDB integration and OpenAI embeddings for semantic search capabilities.

Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN (Convolutional Neural Network) and Word Embeddings on Tensorflow.

EdgeChains.js is Full-Stack GenAI library. Front-end, backend, apis, prompt management, distributed computing. All core prompts & chains are managed declaratively in jsonnet (and not hidden in classes)

[CVPR 2022 Oral] Balanced MSE for Imbalanced Visual Regression https://arxiv.org/abs/2203.16427

?️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库

[ICLR 2024 Oral] Supervised Pre-Trained 3D Models for Medical Image Analysis (9,262 CT volumes + 25 annotated classes)

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

An implementation of the focal loss to be used with LightGBM for binary and multi-class classification problems

An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, generalized to the multi-class case.

A model-agnostic Ruby Generative AI DSL and framework. Provides base classes for building Generators, Actions, Tasks, and Agents that can be used to build AI powered applications in Ruby.

Here, we use a conditional deep convolutional generative adversarial network (cDCGAN) to inverse design across multiple classes of metasurfaces. Reference: https://onlinelibrary.wiley.com/doi/10.1002/adom.202100548

A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430

Yandex SDA classes on deep learning. Version of year 2017

A repository contains a collection of resources and papers on Imbalance Learning On Graphs