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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/dpbayes/napsu-mq-experiments

공식 코드

Noise-Aware Statistical Inference with Differentially Private Synthetic Data

https://github.com/dr-aheydari/SoftAdapt

공식 코드

SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

https://github.com/ds4dh/GLiNER-biomed

공식 코드

GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition

https://github.com/eachsheep/ragsynth

공식 코드

RAGSynth: Synthetic Data for Robust and Faithful RAG Component Optimization

https://github.com/ebiquity/policy_enforced_data_generation

공식 코드

Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis

https://github.com/edm-research/cad2render

공식 코드

CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry

https://github.com/edm-research/cut-and-splat

공식 코드

Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data Generation

https://github.com/eleutherai/gpt-neox

공식 코드

Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models

https://github.com/eliabntt/GRADE-RR

공식 코드

Learning from synthetic data generated with GRADE

https://github.com/epfl-dlab/synthie

공식 코드

Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction

https://github.com/epoch-research/data-stock

공식 코드

Will we run out of data? Limits of LLM scaling based on human-generated data

https://github.com/eth-sri/cuts

공식 코드

CuTS: Customizable Tabular Synthetic Data Generation

https://github.com/eth-sri/synthpai

공식 코드

A Synthetic Dataset for Personal Attribute Inference

https://github.com/facebookresearch/reasonir

공식 코드

ReasonIR: Training Retrievers for Reasoning Tasks

https://github.com/fjxmlzn/DoppelGANger

공식 코드

Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

https://github.com/ganevgv/dp-generative-models

공식 코드

dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation

https://github.com/gianlucatruda/TableDiffusion

공식 코드

Generating tabular datasets under differential privacy

https://github.com/gibson210/gec-depend

공식 코드

GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding

https://github.com/gist-ailab/SF-Mask-RCNN

공식 코드

Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data

https://github.com/goodarzmehr/simbev

공식 코드

SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset

https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction

공식 코드

Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models

https://github.com/google-research/bleurt

공식 코드

Learning Compact Metrics for MT

https://github.com/google-research/google-research/tree/master/true_teacher

공식 코드

TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models

https://github.com/guijinson/fiog

공식 코드

Beyond Classification: Financial Reasoning in State-of-the-Art Language Models

https://github.com/guillermo-jimenez/delineatorswitchandcompose

공식 코드

Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation

https://github.com/hanklee97121/bear_bgp_event_analysis

공식 코드

BEAR: BGP Event Analysis and Reporting

https://github.com/hao-ai-lab/vllm-ltr

공식 코드

Efficient LLM Scheduling by Learning to Rank

https://github.com/haon-chen/SPEED

공식 코드

Little Giants: Synthesizing High-Quality Embedding Data at Scale

https://github.com/harshvivek14/RoundTripOCR

공식 코드

RoundTripOCR: A Data Generation Technique for Enhancing Post-OCR Error Correction in Low-Resource Devanagari Languages

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

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

https://github.com/howiehwong/unigen

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UniGen: A Unified Framework for Textual Dataset Generation Using Large Language Models

https://github.com/hugopstackhouse/MPSTime.jl

공식 코드

Using matrix-product states for time-series machine learning

https://github.com/huilinchenjn/upc_sdg

공식 코드

Privacy-Preserving Synthetic Data Generation for Recommendation Systems

https://github.com/hylz-2019/v2v

공식 코드

V2V: Scaling Event-Based Vision through Efficient Video-to-Voxel Simulation

https://github.com/ibm/api-blend

공식 코드

API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs

https://github.com/ibrahimethemhamamci/generatect

공식 코드

GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes

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