Code for reproducing Manifold Mixup results (ICML 2019)

This repository implements our ACL Findings 2022 research paper RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction. The goal of Zero-Shot Relation Triplet Extraction (ZeroRTE) is to extract relation triplets of the format (head entity, tail entity, relation), despite not having annotated data …

The official GitHub page for the survey paper "A Survey on Data Augmentation in Large Model Era"

[EMNLP 2022 Findings] Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study

Implement mosaic image augmentation with YOLO format

Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation

An example of how to automate the process of training data generation through gprMax for use in machine learning models.

Official release of the DMControl Generalization Benchmark 2 (DMC-GB2)

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

A binary-classifier(pneumonia vs normal) in xray14

This solution is designed to help you unlock legacy mainframe data by migrating data files from mainframe systems to AWS. By migrating the data, you can make use of the powerful analytics, machine learning, and other services available in AWS to gain insights and make better decisions based on the data.

Data Augmentation For Object Detection using Deep Learning with PyTorch

Data augmentation for time series classification

Main idea of this project was making some new data using simple registration technique from existing CT data. This code be a possible alternative of arbitrary augmentation such as flip, rotation, zoom etc.

Traffic Sign Detection CNN app

Contrastive Learning mit Stable Diffusion-basierter Datenaugmentation: Verbesserung der Bildklassifikation durch synthetische Daten

This repository is a collection of PyTorch code examples, covering beginner to advanced topics, and including implementation of CNN models from scratch.

Classifies whether an image is of a dog or cat using pre-trained models

Script to randomly apply Geometric Transformations/Noise on an image, generating an output slightly different (or not). This is usually used in Machine Learning for data augmentation.