Papers with Code 아카이브

Synthetic Data Generation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 2
평가표: 2
보존된 평가 기록: 4

The generation of tabular data by any means possible.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/1712n/eltex

공식 코드

ELTEX: A Framework for Domain-Driven Synthetic Data Generation

https://github.com/3dperceptionlab/unrealrox

공식 코드

UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation

https://github.com/3dperceptionlab/unrealrox-plus

공식 코드

UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments

https://github.com/AugustDS/synthetic-medical-benchmark

공식 코드

Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation

https://github.com/BatFaceWayne/POV_Surgery

공식 코드

POV-Surgery: A Dataset for Egocentric Hand and Tool Pose Estimation During Surgical Activities

https://github.com/BerkeleyLearnVerify/Scenic

공식 코드

Scenic: A Language for Scenario Specification and Scene Generation

https://github.com/BradSegal/CXR_PGGAN

공식 코드

Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs

https://github.com/BradSegal/ClinicalDataSimulator

공식 코드

Bridging the Generalisation Gap: Synthetic Data Generation for Multi-Site Clinical Model Validation

https://github.com/CDInstitute/Building-Dataset-Generator

공식 코드

Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture

https://github.com/Cardio-AI/memorization-ldm

공식 코드

Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data

https://github.com/DIVA-DIA/Generating-Synthetic-Handwritten-Historical-Documents

공식 코드

Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs

https://github.com/DPBayes/data-sharing-examples

공식 코드

Privacy-preserving data sharing via probabilistic modelling

https://github.com/DPBayes/twinify

공식 코드

Noise-Aware Statistical Inference with Differentially Private Synthetic Data

https://github.com/DPautoGAN/DPautoGAN

공식 코드

Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning

https://github.com/DS4SD/MarkushGrapher

공식 코드

MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures

https://github.com/DatrikIntelligence/D3A-TS-Denoising-Driven-Data-Augmentation-in-Time-Series

공식 코드

D3A-TS: Denoising-Driven Data Augmentation in Time Series

https://github.com/Ezharjan/AutoSceneGen

공식 코드

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner

https://github.com/GGLAB-KU/gecturk

공식 코드

GECTurk: Grammatical Error Correction and Detection Dataset for Turkish

https://github.com/GalYona21/Neural-Descriptors

공식 코드

Neural Descriptors: Self-Supervised Learning of Robust Local Surface Descriptors Using Polynomial Patches

https://github.com/IBM/TabFormer

공식 코드

Tabular Transformers for Modeling Multivariate Time Series

https://github.com/JTRNEO/SynRS3D

공식 코드

SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery

https://github.com/JonasNasimzada/LetsPlay4Emotion

공식 코드

Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints

https://github.com/KID-22/CTAR

공식 코드

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

https://github.com/KieDani/SpinAndTrajectoryTableTennis

공식 코드

Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer

https://github.com/Lattle-y/AI-recognition-for-lq-ed

공식 코드

A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawings

https://github.com/MMehdiMousavi/SuperCaustics

공식 코드

SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications

https://github.com/MiraPurkrabek/RePoGen

공식 코드

Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data

https://github.com/NatLibFi/Annif

공식 코드

Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs

https://github.com/Nisha1803/Methodology_Endosomal_Escape

공식 코드

Stochastic Model of siRNA Endosomal Escape Mediated by Fusogenic Peptides in OVCAR-3

https://github.com/NomiMalik0207/PedSynth-and-PedGNN-for-Pedestrian-Intention-Prediction

공식 코드

Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction

https://github.com/ParkLabML/DP-MERF

공식 코드

DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation

https://github.com/PerceivingSystems/bedlam_render

공식 코드

BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion

https://github.com/RRFar/Quantum-Downsampling-Filter

공식 코드

Quantum Down Sampling Filter for Variational Auto-encoder

https://github.com/Samuel-Maddock/flaim

공식 코드

FLAIM: AIM-based Synthetic Data Generation in the Federated Setting

https://github.com/SeyedMuhammadHosseinMousavi/Synthetic-Data-Generation-by-Supervised-Neural-Gas-Network

공식 코드

Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data

https://github.com/SeyedMuhammadHosseinMousavi/Synthetic-Data-Generation-of-Body-Motion-Data-by-Neural-Gas-Network-for-Emotion-Recognition

공식 코드

Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.