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

A trusted repository for groundbreaking EEG research code. Some peer-reviewed algorithms (such as EEG data augmentation techniques, EEG classification models) to push the boundaries of neuroscience.

This repository contains all the source code needed to reproduce the experiments or review the results obtained in the research paper "D3A-TS: Denoising-Driven Data Augmentation in Time Series"

Backtranslations of IMDB movie reviews for Data Augmentation Purposes

Analysis of the Olivetti Faces dataset using various machine learning and deep learning methods. Along with sentiment analysis of the IMDB Movie Review Dataset employing models ranging from Naive Bayes to Recurrent Neural Networks (RNNs), including data augmentation techniques.

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success

Classification of product reviews using RuBERT, XLM-RoBERTa, CatBoost, and LoRA, including data augmentation with back translation, to improve performance on minor classes.

This repository contains the official implementation of "Data-Agnostic Augmentations for Unknown Variations - Out-of-Distribution Generalisation in MRI Segmentation", under review at MIDL 2025.

Sentiment Analysis Project uses the Amazon reviews dataset to classify sentiments with deep neural network (DNN) classifiers. I applied data augmentation techniques to improve model performance, demonstrating advanced machine learning methods for analyzing consumer feedback.

Code for the paper "Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation" (under review)

Built a hybrid sentiment model using BERT-based augmentation, transformer embeddings, ensemble learners, and a CNN-BiGRU-Attention network. Reached 91.15% accuracy on Amazon reviews via stacking and Optuna hyperparameter tuning. Used contextual augmentation, mpnet embeddings, and SMOTE to improve class balance and data quality

Project2_data-augmentation-review

No description available

Data Augmentation to Identify Relevant Reviews forProduct Question Answering

A review of data augmentation methods and a proposed new method based on principal component analysis (PCA).

Production-grade MLOps data pipeline for Yelp restaurant review sentiment analysis with quality checks, preprocessing, augmentation, and performance benchmarking

This project have review recently accessed data augmentation paper(today 2021), and 3 idea(Gray Box, Stripe Cutout, SaliencyRicap)

This project involves analyzing hotel review data using Natural Language Processing (NLP) techniques. The project is divided into several steps, starting with Exploratory Data Analysis (EDA) and progressing to data augmentation, modeling, and iterative improvements.

In this project, I looked at the IMDb movie reviews data set and performed a sentiment analysis using BERT and then compared the change in accuracy after applying an Easy Data Augmentation.

Sentiment Analysis with DistilBERT: A lightweight and efficient transformer-based solution for classifying movie reviews as positive or negative, leveraging IMDb dataset, data augmentation, and mixed precision training.

A text classification model that predicts the star rating of a customer's written Yelp review. Uses a variation of the BERT neural-network architecture, with data augmentation and text normalization for data pre-processing.

Sentiment Analysis & Review Classification with Deep Learning A robust NLP pipeline for classifying reviews into 5 categories (Bad to Excellent) , Features advanced data engineering including Back-Translation, Spell Checking, GloVe Embeddings, and NLTK Augmentation, culminating in an ensemble model for maximum accuracy.

A deep learning project for sentiment classification of English text reviews into 5 classes (from 1 to 5), using a Bidirectional GRU-based Recurrent Neural Network with transfer learning and data augmentation.

A sentiment analysis project for movie reviews that enhances data quality through back-translation augmentation using MarianMT models. The project explores multiple text representation techniques — Bag of Words, TF-IDF, and Word2Vec — and evaluates their performance using Logistic Regression and Naive Bayes classifiers.

This Project implements an automated capsule endoscopy image classification system using the InceptionV3 deep learning model. It classifies gastrointestinal images into ulcers, polyps, bleeding, normal tissue using transfer learning, data augmentation, and CNN-based feature extraction to improve diagnostic accuracy and reduce manual review effort.

Iris Flower Classification, Recognizing CIFAR-10 images (Part I - Simple model), Recognizing CIFAR-10 images (Part II - Improved model), Recognizing CIFAR-10 images (Part III - Data Augmentation), Traffic Sign Recognition using Deep Learning, Movie Recommendation Engine, Linear Regression, Multivariate Linear Regression, Sentiment Analysis of Movie Reviews, Wine quality prediction, Unsupervised Learning, Autoencoders using Fashion MNIST, Logistic Regression, Fuzzy string matching, Spam email classification, Customer churn prediction & Predicting Credit Card Approvals.