Popular GitHub Repositories for Augmentations

RAD: Reinforcement Learning with Augmented Data

Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)

Generation and evaluation of synthetic time series datasets (also, augmentations, visualizations, a collection of popular datasets) NeurIPS'24

Train Scene Graph Generation for Visual Genome and GQA in PyTorch >= 1.2 with improved zero and few-shot generalization.

[CVPR 2022] "Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations" by Tianlong Chen*, Peihao Wang*, Zhiwen Fan, Zhangyang Wang

[ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut, ICML 2022.

official implemntation for "Contrastive Learning with Stronger Augmentations"

[ECCV 2024] Official Repo for: Dataset Enhancement with Instance-Level Augmentations

[KDD'22] Source codes of "Graph Rationalization with Environment-based Augmentations"

RuTransform: python framework for adversarial attacks and text data augmentation for Russian

In this work I investigate the speech command task developing and analyzing deep learning models. The state of the art technology uses convolutional neural networks (CNN) because of their intrinsic nature of learning correlated represen- tations as is the speech. In particular I develop different CNNs trained on the Google Speech Command Dataset and tested on different scenarios. A main problem on speech recognition consists in the differences on pronunciations of words among different people: one way of building an invariant model to variability is to augment the dataset perturbing the input. In this work I study two kind of augmentations: the Vocal Tract Length Perturbation (VTLP) and the Synchronous Overlap and Add (SOLA) that locally perturb the input in frequency and time respectively. The models trained on augmented data outperforms

A PyTorch implementation of "Self-Supervised GNN that Jointly Learns to Augment" or "Jointly Learnable Data Augmentations for Self-Supervised GNNs" papers, which appeared at Neurips2021 Workshop on Self-Supervised Learning - Theory and Practice

EMNLP 2022 "PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings"

[KDD23] Official PyTorch implementation for "Improving Conversational Recommendation Systems via Counterfactual Data Simulation".

[NeurIPS 2023, KDD MLG 2023] Repo that contains code for the paper titled: "FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations".

A ready-to-use pytorch dataloader for audio classification, speech classification, speaker recognition, etc. with in-GPU augmentations

PyTorch Implementation of AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation (ECCV 2024)

YOLOv12 Underwater Object Detection is an open-source suite for underwater object detection, built on YOLOv12. It offers an end-to-end pipeline with GPU-accelerated training, customizable data augmentations, real-time inference via Gradio, and support for model export (ONNX & PyTorch).

Audio data loading and augmentations in JAX