Papers with Code 아카이브
Synthetic Data Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/manestay/paxqa
공식 코드PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale
https://github.com/manjunath-mahendra/NextConvGeN
공식 코드https://github.com/marti5ini/gencda
공식 코드Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery
https://github.com/martin-3240/safesea
공식 코드SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions
https://github.com/matejklemen/slonspell
공식 코드Neural spell-checker: Beyond words with synthetic data generation
https://github.com/meghdadk/DDUp
공식 코드Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
https://github.com/megvii-research/NAFNet
공식 코드FSID: Fully Synthetic Image Denoising via Procedural Scene Generation
https://github.com/megvii-research/megactor
공식 코드MegActor: Harness the Power of Raw Video for Vivid Portrait Animation
https://github.com/megvii-research/megfaceanimate
공식 코드MegActor: Harness the Power of Raw Video for Vivid Portrait Animation
https://github.com/michaelchiucw/smoclust
공식 코드SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams
https://github.com/michalislazarou/tfh_fewshot
공식 코드https://github.com/michigannlp/depression_synthetic_data
공식 코드https://github.com/microsoft/digiface1m
공식 코드DigiFace-1M: 1 Million Digital Face Images for Face Recognition
https://github.com/miguel-fuentes/jam_aistats
공식 코드Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
https://github.com/mingchenli/MCGEN-Private-Synthetic-Data-Generator
공식 코드MC-GEN:Multi-level Clustering for Private Synthetic Data Generation
https://github.com/miulab/converser
공식 코드CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation
https://github.com/mohdkhalil/repository-supplementary-for-lak-25-paper--creating-artificial-students-that-never-existed
공식 코드https://github.com/moisesrsantos/tsmorph_aequitas
공식 코드https://github.com/mosh98/text_aug_low_res
공식 코드Distributional Data Augmentation Methods for Low Resource Language
https://github.com/mostly-ai/mostlyai-engine
공식 코드https://github.com/mostly-ai/mostlyai-qa
공식 코드Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework
https://github.com/mountstonne/dggan
공식 코드Synthetic Demographic Data Generation for Card Fraud Detection Using GANs
https://github.com/mraoaakash/mraoaakash-capstone-thesis
공식 코드Improving text-conditioned latent diffusion for cancer pathology
https://github.com/msalhab96/araspot
공식 코드https://github.com/mvrl/context-cir
공식 코드ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval
https://github.com/mxschmdt/mrqa-gen-al
공식 코드Combining Data Generation and Active Learning for Low-Resource Question Answering
https://github.com/mxschmdt/mrqa-prompting-gen
공식 코드Prompting-based Synthetic Data Generation for Few-Shot Question Answering
https://github.com/mzelling/repliclust
공식 코드Natural Language-Based Synthetic Data Generation for Cluster Analysis
https://github.com/natlibfi/annif-llms4subjects
공식 코드https://github.com/neulab/prompt2model
공식 코드Better Synthetic Data by Retrieving and Transforming Existing Datasets
https://github.com/nfdi4health/vambn-extensions-evaluations
공식 코드https://github.com/ngzhili/syntable
공식 코드https://github.com/nighood/SynAlign
공식 코드https://github.com/nikoguan/p_ocr
공식 코드Advancing Post-OCR Correction: A Comparative Study of Synthetic Data
https://github.com/njudeepengine/llm_based_atp
공식 코드LLM-based Automated Theorem Proving Hinges on Scalable Synthetic Data Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.