There are 1 repository under data-augmentation-strategies topic.
[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"
[CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".
Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut, ICML 2022.
Data Augmentation For Object Detection using Pytorch and PIL
The official implementation of ACL 2020, "Logic-Guided Data Augmentation and Regularization for Consistent Question Answering".
The source code and pre-trained models for Motion Matters: Neural Motion Transfer for Better Camera Physiological Sensing (WACV 2024, Oral).
[IEEE RA-L 2023] Towards Better Data Exploitation In Self-Supervised Monocular Depth Estimation
Unofficial Pytorch Implementation Of AdversarialAutoAugment(ICLR2020)
Projet-PI-4DS2
[KDD23] Official PyTorch implementation for "Improving Conversational Recommendation Systems via Counterfactual Data Simulation".
Neural Fuzzy Repair (NFR) is a data augmentation pipeline, which integrates fuzzy matches (i.e. similar translations) into neural machine translation.
[KDD23] Official PyTorch implementation for "Improving Conversational Recommendation Systems via Counterfactual Data Simulation".
Codes for employing PySimMIBCI for MI-EEG data generation and for using such data with FBCNetToolbox models
Extra bits of unsanitized code for plotting, training, etc. related to our CVPR 2021 paper "Augmentation Strategies for Learning with Noisy Labels".
A toolkit to augment audios (e.g. noise, reverb, distort, speedup, packet loss, farfield effects).
[ACL'2023 Oral] "Learning to Substitute Span towards Improving Compositional Generalization"
Pytorch implementation of the paper: "BMN: Boundary-Matching Network for Temporal Action Proposal Generation", along with three new modules to address overfitting issues found in the baseline model, and their ablation studies.
Augmentation for CV using frequency shortcuts
Unleashing the Power of CNNs for Precise American Sign Language Recognition.
This is the official source code of the paper 'Features kept generative adversarial network data augmentation strategy for hyperspectral image classification'
The purpose of this research project is to compare traditional CNNs to vision transformers, can transformers give a higher AUC when classifying Atypical Femoral Fracture / Normal Femoral Fracture?
Node Duplication Improves Cold-start Link Prediction
This is an effort to provide different approaches towards human action recognition from video. A method to perform data augmentation on skeletal data so as to achieve a view independent recognition approach is included.
Applying data augmentation to deep-learning-based (CNN) image classification task.
1st Place (🏆) for Best Face Recognition System in the Face & Gesture Analysis Challenge at UPF
A study that aims to unfold what emotions did Filipino students manifest during a year of Covid-19 quarantines.
2nd place, 2023 AI Innovation Challenge (정보통신기획평가원장상)
Spotify Classification Problem 2023
Disease - Symptom Dataset Cleaning and Augmenting Process
My demonstration of RNN, LSTM, Dropouts, and Data Augmentation Techniques for Texts using Spam and Ham Dataset.
This project investigates the impact of generative AI on the performance of convolutional neural networks (CNNs) in image classification tasks, specifically in the context of limited data.
A Python library for advanced and novel data augmentation, combining traditional techniques like cropping and blurring with state-of-the-art generative AI methods such as style transfer, image inpainting, and latent space interpolation. It boosts data diversity for robust machine learning applications.
This repository contains code and resources for building a Convolutional Neural Network (CNN) that can recognize American Sign Language (ASL) images. The model is capable of classifying letters in ASL using high accuracy. The process involves data preprocessing, creating a CNN model, training, evaluation, and utilizing the trained model to recogniz