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Synthetic Data Generation

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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/shubham1810/trove_toolkit

공식 코드

TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments

https://github.com/soham97/mellow

공식 코드

Mellow: a small audio language model for reasoning

https://github.com/spaceml-org/expanding-sdo-capabilities

공식 코드

Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona

https://github.com/sstocksieker/DAVID

공식 코드

Data Augmentation with Variational Autoencoder for Imbalanced Dataset

https://github.com/syndara-lab/debiased-generation

공식 코드

Debiasing Synthetic Data Generated by Deep Generative Models

https://github.com/synthetic-society/recon-synth

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A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data

https://github.com/tal-aurorax/resofilter

공식 코드

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis

https://github.com/thanosm97/gsoc2022-openvino

공식 코드

Post-training Model Quantization Using GANs for Synthetic Data Generation

https://github.com/tmcarvalho/privatesmote

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Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control

https://github.com/togethercomputer/smir

공식 코드

SMIR: Efficient Synthetic Data Pipeline To Improve Multi-Image Reasoning

https://github.com/tommaso-ferracci/Targeted_Augmentation_Amex

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Targeted synthetic data generation for tabular data via hardness characterization

https://github.com/topo-epfl/crossloc

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CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data

https://github.com/tumftm/sim2realdistributionaligneddataset

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Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level

https://github.com/twak/winsyn_metadata

공식 코드

WinSyn: A High Resolution Testbed for Synthetic Data

https://github.com/ucla-trustworthy-ai-lab/autodiffusion

공식 코드

AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing

https://github.com/unnatural-language/sim2real

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Unnatural Language Processing: Bridging the Gap Between Synthetic and Natural Language Data

https://github.com/upiterbarg/lintseq

공식 코드

Training Language Models on Synthetic Edit Sequences Improves Code Synthesis

https://github.com/vanderschaarlab/data-centric-synthetic-data

공식 코드

Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark

https://github.com/vcerqueira/experiments-online_augmentation

공식 코드

Online Data Augmentation for Forecasting with Deep Learning

https://github.com/vidmodex/vidmodex

공식 코드

VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional Spaces

https://github.com/vivikar/central-sulcus-analysis

공식 코드

SYNCS: Synthetic Data and Contrastive Self-Supervised Training for Central Sulcus Segmentation

https://github.com/vlbthambawita/singan-seg-polyp

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SinGAN-Seg: Synthetic training data generation for medical image segmentation

https://github.com/weiyifan1023/senator

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Structural Entropy Guided Agent for Detecting and Repairing Knowledge Deficiencies in LLMs

https://github.com/wenkaixl/npksd

공식 코드

A Kernelised Stein Statistic for Assessing Implicit Generative Models

https://github.com/xalentis/syntheticeeg

공식 코드

A Statistical Approach for Synthetic EEG Data Generation

https://github.com/xxh43/ukmd

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Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections

https://github.com/yc4ny/SVAD

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SVAD: From Single Image to 3D Avatar via Synthetic Data Generation with Video Diffusion and Data Augmentation

https://github.com/yefyuan/syneval

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A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models

https://github.com/yifei-liu-stat/pass

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Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification

https://github.com/yifei-liu-stat/syn

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Boosting Data Analytics With Synthetic Volume Expansion

https://github.com/yihaop/dreamstruct

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DreamStruct: Understanding Slides and User Interfaces via Synthetic Data Generation

https://github.com/yihedeng9/duoguard

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DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM Guardrails

https://github.com/yjzhao1019/ccup

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CCUP: A Controllable Synthetic Data Generation Pipeline for Pretraining Cloth-Changing Person Re-Identification Models

https://github.com/yr234/tl-for-tsc

공식 코드

Transfer learning for time series classification using synthetic data generation

https://github.com/yueeeeeeee/recsys-extraction-attack

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Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction

https://github.com/yy6linda/synthetic-ehr-benchmarking

공식 코드

A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models

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