44 skills found · Page 1 of 2
AntreasAntoniou / DAGANDAGAN: Data Augmentation Generative Adversarial Networks
khawar-islam / DiffuseMixOfficial PyTorch implementation of DiffuseMix : Label-Preserving Data Augmentation with Diffusion Models (CVPR'2024)
imsb-uke / ScGANRealistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks
yuanmengzhixing / AugGAN Cross Domain Adaptation With GAN Based DataAugmentationAdvanced data augmentation with Generative Adversarial Networks for vehicle detection
BMIRDS / HistoGANCode for "Generative Image Translation for Data Augmentation in Colorectal Histopathology Images" full paper at ML4H Workshop at NeurIPS 2019.
sajadn / Exemplar VAEExemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
heheyas / MeshGen[CVPR 2025 Highlight] MeshGen: Generating PBR Textured Mesh with Render-Enhanced Auto-Encoder and Generative Data Augmentation
DrSasanBarak / AugmentTSAugmentTS :: Time Series Data Augmentation using Deep Generative Models
aim-uofa / DiverGenDiverGen (CVPR 2024) & BSGAL (ICML 2024)
harshitAgr / Brain Synthesis Lesion SegmentationCode for the paper "Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks" and NVIDIA DLI workshop on GAN for Medical Imaging
thu-ml / VflowOfficial code for "VFlow: More Expressive Generative Flows with Variational Data Augmentation" (ICML 2020)
yunxiangfu2001 / DreamDADreamDA: Generative Data Augmentation with Diffusion Models (Official Implementation)
ML-GSAI / Understanding GDA[NeurIPS 2023] Official implementation for our paper "Toward Understanding Generative Data Augmentation".
filippos1994 / Gan Mri AugRepo of the paper "Generative Adversarial Networks as an advanced data augmentation technique for MRI data" by Filippos Konidaris, Thanos Tagaris, Maria Sdraka and Andreas Stafylopatis
Sreyan88 / ACLMCode for ACL 2023 Paper: ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
jscriptcoder / Data Augmentation Using CGANData augmentation using Conditional Generative Adversarial Networks (cGAN)
Mulham91 / Multi Spectral Image Synthesis For Crop Weed Segmentation In Precision FarmingIn this work, we propose an alternative solution with respect to the common data augmentation techniques, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared information, generating four channel multi-spectral synthetic images.
yangyiben / G DAUG C Generative Data Augmentation For Commonsense ReasoningNo description available
greentfrapp / DopingCode for DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
bhavyamerchant / Hyperspec WGANSynthetic Data Augmentation using a Generative Adversarial Network for Improved Hyperspectral Image Classification
