A system for quickly generating training data with weak supervision
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Updated
Aug 9, 2020 - Python
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A system for quickly generating training data with weak supervision
A library containing both highly optimized building blocks and an execution engine for data pre-processing in deep learning applications
High-Level Training, Data Augmentation, and Utilities for Pytorch
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
Data augmentation for NLP, presented at EMNLP 2019
Data Augmentation For Object Detection
Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need
yolo(v3/v4) implementation in keras and tensorflow 2.2
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。
List of useful data augmentation resources. You will find here some not common techniques, libraries, links to github repos, papers and others.
Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST
Efficient Learning of Augmentation Policy Schedules
Some of the available datasets are downloaded in an uncompressed format. For example, Colin27 version 2008 takes 1014 MB. Some storage could be saved if the images were compressed after downloading.
Deep Convolutional Neural Networks for Musical Source Separation
Light-weight Single Person Pose Estimator
A Implementation of SpecAugment with Tensorflow & Pytorch, introduced by Google Brain
Implementation of the mixup training method
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
DeltaPy - Tabular Data Augmentation (by @firmai)
A treasure chest for image classification powered by PaddlePaddle
Data augmentation tool for images
Streaming over lightweight data transformations
Kaldi-based Korean ASR (한국어 음성인식) open-source project
An implementation of "mixup: Beyond Empirical Risk Minimization"
DrQ: Data regularized Q
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We should allow data augmentation using masked-token prediction models through
WordSwapMaskedLM. This would leverage the power of transformers like BERT to generate augmented inputs.