A system for quickly generating training data with weak supervision

A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.

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

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.

Medical imaging toolkit for deep learning

A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning.

一键中文数据增强包 ； NLP数据增强、bert数据增强、EDA：pip install nlpcda

List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.

fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.

Data augmentation for NLP, presented at EMNLP 2019

自然语言处理（nlp），小姜机器人（闲聊检索式chatbot），BERT句向量-相似度（Sentence Similarity），XLNET句向量-相似度（text xlnet embedding），文本分类（Text classification）， 实体提取（ner，bert+bilstm+crf），数据增强（text augment, data enhance），同义句同义词生成，句子主干提取（mainpart），中文汉语短文本相似度，文本特征工程，keras-http-service调用

Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"

An implement of the paper of EDA for Chinese corpus.中文语料的EDA数据增强工具。NLP数据增强。论文阅读笔记。

Data Augmentation For Object Detection

Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.

Collection of papers and resources for data augmentation for NLP.

Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need

Awesome papers about generative Information Extraction (IE) using Large Language Models (LLMs)

Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST