[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".

torch data augmentation toolbox (supports affine transform)

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

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"

Extra bits of unsanitized code for plotting, training, etc. related to our CVPR 2021 paper "Augmentation Strategies for Learning with Noisy Labels".

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

Computational toolkit for efficient prediction of the thickness of 2D materials