#Data-augmentation
Showing 51 of 51 repositories tagged #data-augmentation, ranked by stars
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 processing for AI applications.
A Python library for audio data augmentation. Useful for making audio ML models work well in the real world, not just in the lab.
🎨 NeMo Data Designer: Generate high-quality synthetic data from scratch or from seed data.
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.
List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.
Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"
This repository collects papers for "A Survey on Knowledge Distillation of Large Language Models". We break down KD into Knowledge Elicitation and Distillation Algorithms, and explore the Skill & Vertical Distillation of LLMs.
Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need
Collection of papers and resources for data augmentation for NLP.
A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
Light-weight Single Person Pose Estimator
Copy-paste augmentation for segmentation and detection tasks
DeltaPy - Tabular Data Augmentation (by @firmai)
Augmentation pipeline for rendering synthetic paper printing, faxing, scanning and copy machine processes
Efficient Learning of Augmentation Policy Schedules
Next-generation Albumentations: dual-licensed for open-source and commercial use
Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Generation and evaluation of synthetic time series datasets (also, augmentations, visualizations, a collection of popular datasets) NeurIPS'24
Explore concepts like Self-Correct, Self-Refine, Self-Improve, Self-Contradict, Self-Play, and Self-Knowledge, alongside o1-like reasoning elevation🍓 and hallucination alleviation🍄.
A dataset for training/evaluating Question Answering Retrieval models on ChatGPT responses with the possibility to training/evaluating on real human responses.
A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working in computer vision who require efficient and scalable image augmentation.
Pytorch implementation of Noisy Student Training for Automatic Speech Recognition and Automatic Pronunciation Error Detection problem
Source code for the paper 'Data Augmentation for Skin Lesion Analysis' — 🏆 Best Paper Award at the ISIC Skin Image Analysis Workshop @ MICCAI 2018
A time series signal analysis and classification framework
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
Patho-GAN: interpretation + medical data augmentation. Code for paper work "Explainable Diabetic Retinopathy Detection and Retinal Image Generation"
Data Augmentation For Object Detection using Pytorch and PIL
Multiclass image classification using Convolutional Neural Network
My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]
📈 🐍 Multidimensional synthetic data generation with Copula and fPCA models in Python
TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf
Multiclass semantic segmentation using U-Net architecture combined with strong image augmentation
Hazy/Dusty Image Synthesis
Monitor people violating Social Distancing or not wearing Face Masks in public through CCTV footage.
Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.
Code to generate realistic synthetic healthcare data with diffusion models
Repository for the experiments described in my paper titled "DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus"
PyTorch research stack for ML multi-factor trading: 213 factors, bias correction, portfolio optimization, and vectorized backtesting.
[ECCV'24 Workshops Oral] DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling
[Medical Image Analysis] Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis
Let ChatGPT (Large Language Models) Serve As Data Annotator and Zero-shot/few-shot Information Extractor.
Code for ACL 2023 Paper: ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
LSTM stock prediction and backtesting