{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/synthetic-data-generation/papers/9","list_of":"/task/synthetic-data-generation","task":"Synthetic Data Generation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":9,"pages_in_order":9,"rows_per_page":100,"rows":[801,822],"of":822,"counts":{"archive_papers_tagged":822,"with_a_code_link":325,"where_syntology_ran_a_sample":52,"not_listed_spam_title":0,"listed":822,"listed_where_code_ran":52,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":46,"every_run_a_failure_of_syntologys_instrument":6,"listed_with_a_run_with_no_instrument_failure":46,"listed_every_run_a_failure_of_syntologys_instrument":6,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/synthetic-data-generation","prev":"/task/synthetic-data-generation/papers/8","next":null,"papers":[{"url":null,"slug":"variational-autoencoders-for-generative","title":"Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors","date":"2019-11-01","arxiv_id":"1911.02369","repositories_listed":0,"syntology":null},{"url":null,"slug":"restyling-data-application-to-unsupervised","title":"Restyling Data: Application to Unsupervised Domain Adaptation","date":"2019-09-24","arxiv_id":"1909.10900","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-writer-adaptation-for-synthetic","title":"Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition","date":"2019-09-18","arxiv_id":"1909.08473","repositories_listed":0,"syntology":null},{"url":null,"slug":"pmu-data-feature-considerations-for-realistic","title":"PMU Data Feature Considerations for Realistic, Synthetic Data Generation","date":"2019-08-14","arxiv_id":"1908.05244","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustable-and-automated-machine-learning","title":"Trustable and Automated Machine Learning Running with Blockchain and Its Applications","date":"2019-08-14","arxiv_id":"1908.05725","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizing-back-translation-in-neural","title":"Generalizing Back-Translation in Neural Machine Translation","date":"2019-06-17","arxiv_id":"1906.07286","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-generation-and-adaption-for","title":"Synthetic Data Generation and Adaption for Object Detection in Smart Vending Machines","date":"2019-04-28","arxiv_id":"1904.12294","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-target-detection-for-search-and-rescue","title":"Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation","date":"2019-04-25","arxiv_id":"1904.11619","repositories_listed":0,"syntology":null},{"url":null,"slug":"coco_ts-dataset-pixel-level-annotations-based","title":"COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation","date":"2019-04-01","arxiv_id":"1904.00818","repositories_listed":0,"syntology":null},{"url":null,"slug":"depthwisegans-fast-training-generative","title":"DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis","date":"2019-03-06","arxiv_id":"1903.02225","repositories_listed":0,"syntology":null},{"url":null,"slug":"passing-tests-without-memorizing-two-models","title":"Synthetic Data Generators: Sequential and Private","date":"2019-02-09","arxiv_id":"1902.03468","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressing-gans-using-knowledge-distillation","title":"Compressing GANs using Knowledge Distillation","date":"2019-02-01","arxiv_id":"1902.00159","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-vine-copula-models-for-synthetic","title":"Learning Vine Copula Models For Synthetic Data Generation","date":"2018-12-04","arxiv_id":"1812.01226","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-6d-object-pose-estimation-in-cluttered","title":"Robust 6D Object Pose Estimation in Cluttered Scenes using Semantic Segmentation and Pose Regression Networks","date":"2018-10-08","arxiv_id":"1810.03410","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetically-supervised-feature-learning-for","title":"Synthetically Supervised Feature Learning for Scene Text Recognition","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-generation-for-end-to-end","title":"Synthetic data generation for end-to-end thermal infrared tracking","date":"2018-06-04","arxiv_id":"1806.01013","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-generation-for-indic","title":"Synthetic data generation for Indic handwritten text recognition","date":"2018-04-17","arxiv_id":"1804.06254","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-deep-learning-training-for-single","title":"Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction","date":"2018-03-23","arxiv_id":"1803.08840","repositories_listed":0,"syntology":null},{"url":null,"slug":"separating-reflection-and-transmission-images","title":"Separating Reflection and Transmission Images in the Wild","date":"2017-12-06","arxiv_id":"1712.02099","repositories_listed":0,"syntology":null},{"url":null,"slug":"procedural-modeling-and-physically-based","title":"Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications","date":"2017-10-17","arxiv_id":"1710.06270","repositories_listed":0,"syntology":null},{"url":null,"slug":"depthsynth-real-time-realistic-synthetic-data","title":"DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition","date":"2017-02-27","arxiv_id":"1702.08558","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-similarity-for-absent-data","title":"Empirical Similarity for Absent Data Generation in Imbalanced Classification","date":"2015-08-05","arxiv_id":"1508.01235","repositories_listed":0,"syntology":null}],"record_sha256":"4103b8f761cc949991457cb4710e588fd3a0047cff31d06696c4ef91e8892ad7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}