{"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/data-augmentation/papers/20","list_of":"/task/data-augmentation","task":"Data Augmentation","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":20,"pages_in_order":84,"rows_per_page":100,"rows":[1901,2000],"of":8378,"counts":{"archive_papers_tagged":8378,"with_a_code_link":3225,"where_syntology_ran_a_sample":692,"not_listed_spam_title":0,"listed":8378,"listed_where_code_ran":692,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":567,"every_run_a_failure_of_syntologys_instrument":125,"listed_with_a_run_with_no_instrument_failure":567,"listed_every_run_a_failure_of_syntologys_instrument":125,"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/data-augmentation","prev":"/task/data-augmentation/papers/19","next":"/task/data-augmentation/papers/21","papers":[{"url":"/paper/high-resolution-synthesis-of-high-density","slug":"high-resolution-synthesis-of-high-density","title":"High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection","date":"2022-09-20","arxiv_id":"2209.09809","repositories_listed":1,"syntology":null},{"url":"/paper/learning-based-radiomic-prediction-of-type-2","slug":"learning-based-radiomic-prediction-of-type-2","title":"SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification","date":"2022-09-20","arxiv_id":"2209.10043","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-data-augmentation-in-knowledge","slug":"rethinking-data-augmentation-in-knowledge","title":"Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation","date":"2022-09-20","arxiv_id":"2209.09841","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rethinking-data-augmentation-in-knowledge#ran","syntology_url":"https://syntology.ai/paper/2209.09841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.09841"}},"official":{"repos":["jwliang007/ikd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/vega-mt-the-jd-explore-academy-translation","slug":"vega-mt-the-jd-explore-academy-translation","title":"Vega-MT: The JD Explore Academy Translation System for WMT22","date":"2022-09-20","arxiv_id":"2209.09444","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-tooth-segmentation-from-3d-dental","slug":"automatic-tooth-segmentation-from-3d-dental","title":"Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Model","date":"2022-09-16","arxiv_id":"2209.08132","repositories_listed":1,"syntology":null},{"url":"/paper/towards-bridging-the-performance-gaps-of","slug":"towards-bridging-the-performance-gaps-of","title":"Towards Bridging the Performance Gaps of Joint Energy-based Models","date":"2022-09-16","arxiv_id":"2209.07959","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/towards-bridging-the-performance-gaps-of#ran","syntology_url":"https://syntology.ai/paper/2209.07959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07959"}},"official":{"repos":["sndnyang/sadajem"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/a-light-recipe-to-train-robust-vision","slug":"a-light-recipe-to-train-robust-vision","title":"A Light Recipe to Train Robust Vision Transformers","date":"2022-09-15","arxiv_id":"2209.07399","repositories_listed":1,"syntology":null},{"url":"/paper/one-shot-synthesis-of-images-and-segmentation","slug":"one-shot-synthesis-of-images-and-segmentation","title":"One-Shot Synthesis of Images and Segmentation Masks","date":"2022-09-15","arxiv_id":"2209.07547","repositories_listed":1,"syntology":null},{"url":"/paper/class-level-logit-perturbation","slug":"class-level-logit-perturbation","title":"Class-Level Logit Perturbation","date":"2022-09-13","arxiv_id":"2209.05668","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-brain-multigraph-population-from-a","slug":"predicting-brain-multigraph-population-from-a","title":"Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification","date":"2022-09-13","arxiv_id":"2209.06005","repositories_listed":1,"syntology":null},{"url":"/paper/doublemix-simple-interpolation-based-data","slug":"doublemix-simple-interpolation-based-data","title":"DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification","date":"2022-09-12","arxiv_id":"2209.05297","repositories_listed":1,"syntology":null},{"url":"/paper/unified-state-representation-learning-under","slug":"unified-state-representation-learning-under","title":"Unified State Representation Learning under Data Augmentation","date":"2022-09-12","arxiv_id":"2209.05302","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-panoptic","slug":"self-supervised-learning-for-panoptic","title":"Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species","date":"2022-09-10","arxiv_id":"2209.04618","repositories_listed":1,"syntology":null},{"url":"/paper/ranking-enhanced-unsupervised-sentence","slug":"ranking-enhanced-unsupervised-sentence","title":"Ranking-Enhanced Unsupervised Sentence Representation Learning","date":"2022-09-09","arxiv_id":"2209.04333","repositories_listed":1,"syntology":null},{"url":"/paper/selective-text-augmentation-with-word-roles","slug":"selective-text-augmentation-with-word-roles","title":"Selective Text Augmentation with Word Roles for Low-Resource Text Classification","date":"2022-09-04","arxiv_id":"2209.01560","repositories_listed":1,"syntology":null},{"url":"/paper/improving-compositional-generalization-in-2","slug":"improving-compositional-generalization-in-2","title":"Improving Compositional Generalization in Math Word Problem Solving","date":"2022-09-03","arxiv_id":"2209.01352","repositories_listed":1,"syntology":null},{"url":"/paper/training-strategies-for-improved-lip-reading","slug":"training-strategies-for-improved-lip-reading","title":"Training Strategies for Improved Lip-reading","date":"2022-09-03","arxiv_id":"2209.01383","repositories_listed":1,"syntology":null},{"url":"/paper/back-to-bones-rediscovering-the-role-of","slug":"back-to-bones-rediscovering-the-role-of","title":"Back-to-Bones: Rediscovering the Role of Backbones in Domain Generalization","date":"2022-09-02","arxiv_id":"2209.01121","repositories_listed":1,"syntology":null},{"url":"/paper/litedepth-digging-into-fast-and-accurate","slug":"litedepth-digging-into-fast-and-accurate","title":"LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices","date":"2022-09-02","arxiv_id":"2209.00961","repositories_listed":1,"syntology":null},{"url":"/paper/random-text-perturbations-work-but-not-always","slug":"random-text-perturbations-work-but-not-always","title":"Random Text Perturbations Work, but not Always","date":"2022-09-02","arxiv_id":"2209.00797","repositories_listed":1,"syntology":null},{"url":"/paper/evit-privacy-preserving-image-retrieval-via","slug":"evit-privacy-preserving-image-retrieval-via","title":"EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing","date":"2022-08-31","arxiv_id":"2208.14657","repositories_listed":1,"syntology":null},{"url":"/paper/style-agnostic-reinforcement-learning","slug":"style-agnostic-reinforcement-learning","title":"Style-Agnostic Reinforcement Learning","date":"2022-08-31","arxiv_id":"2208.14863","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/style-agnostic-reinforcement-learning#ran","syntology_url":"https://syntology.ai/paper/2208.14863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.14863"}},"official":{"repos":["postech-cvlab/style-agnostic-rl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-diffusion-model-predicts-3d-shapes-from-2d","slug":"a-diffusion-model-predicts-3d-shapes-from-2d","title":"A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images","date":"2022-08-30","arxiv_id":"2208.14125","repositories_listed":1,"syntology":null},{"url":"/paper/radial-prediction-domain-adaption-classifier","slug":"radial-prediction-domain-adaption-classifier","title":"Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge","date":"2022-08-29","arxiv_id":"2208.13902","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-neural-network-language-modeling-for","slug":"bayesian-neural-network-language-modeling-for","title":"Bayesian Neural Network Language Modeling for Speech Recognition","date":"2022-08-28","arxiv_id":"2208.13259","repositories_listed":1,"syntology":null},{"url":"/paper/factmix-using-a-few-labeled-in-domain","slug":"factmix-using-a-few-labeled-in-domain","title":"FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition","date":"2022-08-24","arxiv_id":"2208.11464","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/factmix-using-a-few-labeled-in-domain#ran","syntology_url":"https://syntology.ai/paper/2208.11464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.11464"}},"official":{"repos":["lifan-yuan/factmix"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/motion-robust-high-speed-light-weighted","slug":"motion-robust-high-speed-light-weighted","title":"Motion Robust High-Speed Light-Weighted Object Detection With Event Camera","date":"2022-08-24","arxiv_id":"2208.11602","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-on-graphs-for-table-type","slug":"data-augmentation-on-graphs-for-table-type","title":"Data augmentation on graphs for table type classification","date":"2022-08-23","arxiv_id":"2208.11210","repositories_listed":1,"syntology":null},{"url":"/paper/the-value-of-out-of-distribution-data","slug":"the-value-of-out-of-distribution-data","title":"The Value of Out-of-Distribution Data","date":"2022-08-23","arxiv_id":"2208.10967","repositories_listed":1,"syntology":null},{"url":"/paper/threshold-adaptive-unsupervised-focal-loss","slug":"threshold-adaptive-unsupervised-focal-loss","title":"Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation","date":"2022-08-23","arxiv_id":"2208.10716","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-question-answering-via-answer","slug":"unsupervised-question-answering-via-answer","title":"Unsupervised Question Answering via Answer Diversifying","date":"2022-08-23","arxiv_id":"2208.10813","repositories_listed":1,"syntology":null},{"url":"/paper/toward-better-target-representation-for","slug":"toward-better-target-representation-for","title":"RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation","date":"2022-08-22","arxiv_id":"2208.10531","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-analysis-of-mixed-sample-data","slug":"a-unified-analysis-of-mixed-sample-data","title":"A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective","date":"2022-08-21","arxiv_id":"2208.09913","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-visual-place-recognition-by","slug":"self-supervised-visual-place-recognition-by","title":"Self-Supervised Place Recognition by Refining Temporal and Featural Pseudo Labels from Panoramic Data","date":"2022-08-19","arxiv_id":"2208.09315","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-visual-place-recognition-by#ran","syntology_url":"https://syntology.ai/paper/2208.09315","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.09315"}},"official":{"repos":["ai4ce/TF-VPR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mulzdg-multilingual-code-switching-framework","slug":"mulzdg-multilingual-code-switching-framework","title":"MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation","date":"2022-08-18","arxiv_id":"2208.08629","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-privacy-effect-of-data-enhancement-via","slug":"on-the-privacy-effect-of-data-enhancement-via","title":"On the Privacy Effect of Data Enhancement via the Lens of Memorization","date":"2022-08-17","arxiv_id":"2208.08270","repositories_listed":1,"syntology":{"n":17,"n_ran":14,"n_constructed":0,"n_ran_checked":13,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/on-the-privacy-effect-of-data-enhancement-via#ran","syntology_url":"https://syntology.ai/paper/2208.08270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08270"}},"official":{"repos":["lixiaothu/privacy_and_aug"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/pcc-paraphrasing-with-bottom-k-sampling-and","slug":"pcc-paraphrasing-with-bottom-k-sampling-and","title":"PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation","date":"2022-08-17","arxiv_id":"2208.08110","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pcc-paraphrasing-with-bottom-k-sampling-and#ran","syntology_url":"https://syntology.ai/paper/2208.08110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08110"}},"official":{"repos":["hongyuanluke/pcc"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/posetrans-a-simple-yet-effective-pose","slug":"posetrans-a-simple-yet-effective-pose","title":"PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose Estimation","date":"2022-08-16","arxiv_id":"2208.07755","repositories_listed":1,"syntology":null},{"url":"/paper/role-of-data-augmentation-in-unsupervised","slug":"role-of-data-augmentation-in-unsupervised","title":"Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success","date":"2022-08-16","arxiv_id":"2208.07734","repositories_listed":1,"syntology":null},{"url":"/paper/ariel-adversarial-graph-contrastive-learning","slug":"ariel-adversarial-graph-contrastive-learning","title":"ARIEL: Adversarial Graph Contrastive Learning","date":"2022-08-15","arxiv_id":"2208.06956","repositories_listed":1,"syntology":null},{"url":"/paper/syntax-driven-data-augmentation-for-named","slug":"syntax-driven-data-augmentation-for-named","title":"Syntax-driven Data Augmentation for Named Entity Recognition","date":"2022-08-15","arxiv_id":"2208.06957","repositories_listed":1,"syntology":null},{"url":"/paper/renyicl-contrastive-representation-learning","slug":"renyicl-contrastive-representation-learning","title":"RenyiCL: Contrastive Representation Learning with Skew Renyi Divergence","date":"2022-08-12","arxiv_id":"2208.06270","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/renyicl-contrastive-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2208.06270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.06270"}},"official":{"repos":["kyungmnlee/renyicl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/mixskd-self-knowledge-distillation-from-mixup","slug":"mixskd-self-knowledge-distillation-from-mixup","title":"MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition","date":"2022-08-11","arxiv_id":"2208.05768","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mixskd-self-knowledge-distillation-from-mixup#ran","syntology_url":"https://syntology.ai/paper/2208.05768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.05768"}},"official":{"repos":["winycg/self-kd-lib"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/regularizing-deep-neural-networks-with-1","slug":"regularizing-deep-neural-networks-with-1","title":"Regularizing Deep Neural Networks with Stochastic Estimators of Hessian Trace","date":"2022-08-11","arxiv_id":"2208.05924","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/regularizing-deep-neural-networks-with-1#ran","syntology_url":"https://syntology.ai/paper/2208.05924","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.05924"}},"official":{"repos":["iclrsubmission1596/regularizing-deep-neural-networks-with-stochastic-estimators-of-hessian-trace"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/ultra-lite-convolutional-neural-network-for","slug":"ultra-lite-convolutional-neural-network-for","title":"Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios","date":"2022-08-09","arxiv_id":"2208.04659","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-egocentric-hand-object","slug":"fine-grained-egocentric-hand-object","title":"Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications","date":"2022-08-07","arxiv_id":"2208.03826","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-multimodal-feature-extraction-mining","slug":"hybrid-multimodal-feature-extraction-mining","title":"Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis","date":"2022-08-05","arxiv_id":"2208.03051","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-representation-learning-for-rf","slug":"disentangled-representation-learning-for-rf","title":"Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics","date":"2022-08-04","arxiv_id":"2208.02724","repositories_listed":1,"syntology":null},{"url":"/paper/a-feature-space-multimodal-data-augmentation","slug":"a-feature-space-multimodal-data-augmentation","title":"A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval","date":"2022-08-03","arxiv_id":"2208.02080","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-fine-grained-classification","slug":"convolutional-fine-grained-classification","title":"Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization","date":"2022-08-03","arxiv_id":"2208.01997","repositories_listed":1,"syntology":null},{"url":"/paper/realpatch-a-statistical-matching-framework","slug":"realpatch-a-statistical-matching-framework","title":"RealPatch: A Statistical Matching Framework for Model Patching with Real Samples","date":"2022-08-03","arxiv_id":"2208.02192","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/realpatch-a-statistical-matching-framework#ran","syntology_url":"https://syntology.ai/paper/2208.02192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.02192"}},"official":{"repos":["wearepal/realpatch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/subject-specific-lesion-generation-and-pseudo","slug":"subject-specific-lesion-generation-and-pseudo","title":"Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images","date":"2022-08-03","arxiv_id":"2208.02135","repositories_listed":1,"syntology":null},{"url":"/paper/multilingual-coreference-resolution-in","slug":"multilingual-coreference-resolution-in","title":"Multilingual Coreference Resolution in Multiparty Dialogue","date":"2022-08-02","arxiv_id":"2208.01307","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-class-incremental-learning-from-an","slug":"few-shot-class-incremental-learning-from-an","title":"Few-Shot Class-Incremental Learning from an Open-Set Perspective","date":"2022-07-30","arxiv_id":"2208.00147","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/few-shot-class-incremental-learning-from-an#ran","syntology_url":"https://syntology.ai/paper/2208.00147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.00147"}},"official":{"repos":["canpeng123/fscil_alice"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-novel-data-augmentation-technique-for-out","slug":"a-novel-data-augmentation-technique-for-out","title":"A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions","date":"2022-07-28","arxiv_id":"2207.13916","repositories_listed":1,"syntology":{"n":14,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/a-novel-data-augmentation-technique-for-out#ran","syntology_url":"https://syntology.ai/paper/2207.13916","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.13916"}},"official":{"repos":["cnc-ood/cnc_ood"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/depth-field-networks-for-generalizable-multi","slug":"depth-field-networks-for-generalizable-multi","title":"Depth Field Networks for Generalizable Multi-view Scene Representation","date":"2022-07-28","arxiv_id":"2207.14287","repositories_listed":1,"syntology":null},{"url":"/paper/optimization-of-artificial-neural-networks","slug":"optimization-of-artificial-neural-networks","title":"Optimization of Artificial Neural Networks models applied to the identification of images of asteroids' resonant arguments","date":"2022-07-28","arxiv_id":"2207.14181","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-hypergraph-transformer-for","slug":"self-supervised-hypergraph-transformer-for","title":"Self-Supervised Hypergraph Transformer for Recommender Systems","date":"2022-07-28","arxiv_id":"2207.14338","repositories_listed":1,"syntology":null},{"url":"/paper/aadg-automatic-augmentation-for-domain","slug":"aadg-automatic-augmentation-for-domain","title":"AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation","date":"2022-07-27","arxiv_id":"2207.13249","repositories_listed":1,"syntology":null},{"url":"/paper/controllable-user-dialogue-act-augmentation","slug":"controllable-user-dialogue-act-augmentation","title":"Controllable User Dialogue Act Augmentation for Dialogue State Tracking","date":"2022-07-26","arxiv_id":"2207.12757","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-classification-with-counterfactual","slug":"efficient-classification-with-counterfactual","title":"Efficient Classification with Counterfactual Reasoning and Active Learning","date":"2022-07-25","arxiv_id":"2207.12086","repositories_listed":1,"syntology":null},{"url":"/paper/ra-depth-resolution-adaptive-self-supervised","slug":"ra-depth-resolution-adaptive-self-supervised","title":"RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation","date":"2022-07-25","arxiv_id":"2207.11984","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/ra-depth-resolution-adaptive-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2207.11984","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.11984"}},"official":{"repos":["hmhemu/ra-depth"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/semi-leak-membership-inference-attacks","slug":"semi-leak-membership-inference-attacks","title":"Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning","date":"2022-07-25","arxiv_id":"2207.12535","repositories_listed":1,"syntology":null},{"url":"/paper/3d-random-occlusion-and-multi-layer","slug":"3d-random-occlusion-and-multi-layer","title":"3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization","date":"2022-07-22","arxiv_id":"2207.10895","repositories_listed":1,"syntology":{"n":8,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":8,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/3d-random-occlusion-and-multi-layer#ran","syntology_url":"https://syntology.ai/paper/2207.10895","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10895"}},"official":{"repos":["xjtlu-cvlab/3drom"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/splitmixer-fat-trimmed-from-mlp-like-models","slug":"splitmixer-fat-trimmed-from-mlp-like-models","title":"SplitMixer: Fat Trimmed From MLP-like Models","date":"2022-07-21","arxiv_id":"2207.10255","repositories_listed":1,"syntology":null},{"url":"/paper/tailoring-self-supervision-for-supervised","slug":"tailoring-self-supervision-for-supervised","title":"Tailoring Self-Supervision for Supervised Learning","date":"2022-07-20","arxiv_id":"2207.10023","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/tailoring-self-supervision-for-supervised#ran","syntology_url":"https://syntology.ai/paper/2207.10023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10023"}},"official":{"repos":["wjun0830/localizable-rotation"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/det6d-a-ground-aware-full-pose-3d-object","slug":"det6d-a-ground-aware-full-pose-3d-object","title":"Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness","date":"2022-07-19","arxiv_id":"2207.09412","repositories_listed":1,"syntology":null},{"url":"/paper/did-m3d-decoupling-instance-depth-for","slug":"did-m3d-decoupling-instance-depth-for","title":"DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection","date":"2022-07-18","arxiv_id":"2207.08531","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":11,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":10,"n_pointer_only":3,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/did-m3d-decoupling-instance-depth-for#ran","syntology_url":"https://syntology.ai/paper/2207.08531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08531"}},"official":{"repos":["spengliang/did-m3d"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/fakeclr-exploring-contrastive-learning-for","slug":"fakeclr-exploring-contrastive-learning-for","title":"FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs","date":"2022-07-18","arxiv_id":"2207.08630","repositories_listed":1,"syntology":null},{"url":"/paper/research-trends-and-applications-of-data","slug":"research-trends-and-applications-of-data","title":"Research Trends and Applications of Data Augmentation Algorithms","date":"2022-07-18","arxiv_id":"2207.08817","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-data-augmentation-for-robust","slug":"rethinking-data-augmentation-for-robust","title":"Rethinking Data Augmentation for Robust Visual Question Answering","date":"2022-07-18","arxiv_id":"2207.08739","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/rethinking-data-augmentation-for-robust#ran","syntology_url":"https://syntology.ai/paper/2207.08739","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08739"}},"official":{"repos":["itemzheng/kddaug"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/tokenmix-rethinking-image-mixing-for-data","slug":"tokenmix-rethinking-image-mixing-for-data","title":"TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers","date":"2022-07-18","arxiv_id":"2207.08409","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/tokenmix-rethinking-image-mixing-for-data#ran","syntology_url":"https://syntology.ai/paper/2207.08409","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08409"}},"official":{"repos":["sense-x/tokenmix"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/progress-and-limitations-of-deep-networks-to","slug":"progress-and-limitations-of-deep-networks-to","title":"Progress and limitations of deep networks to recognize objects in unusual poses","date":"2022-07-16","arxiv_id":"2207.08034","repositories_listed":1,"syntology":null},{"url":"/paper/bootstrap-state-representation-using-style","slug":"bootstrap-state-representation-using-style","title":"Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning","date":"2022-07-15","arxiv_id":"2207.07749","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-bark-beetle-induced-forest","slug":"classification-of-bark-beetle-induced-forest","title":"Classification of Bark Beetle-Induced Forest Tree Mortality using Deep Learning","date":"2022-07-15","arxiv_id":"2207.07241","repositories_listed":1,"syntology":null},{"url":"/paper/towards-better-dermoscopic-image-feature","slug":"towards-better-dermoscopic-image-feature","title":"Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification","date":"2022-07-15","arxiv_id":"2207.07303","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-scalable-recommendation-via","slug":"efficient-and-scalable-recommendation-via","title":"Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation","date":"2022-07-13","arxiv_id":"2207.05959","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-augmentation-for-imbalanced-deep","slug":"efficient-augmentation-for-imbalanced-deep","title":"Efficient Augmentation for Imbalanced Deep Learning","date":"2022-07-13","arxiv_id":"2207.06080","repositories_listed":1,"syntology":null},{"url":"/paper/label-efficient-self-supervised-speaker","slug":"label-efficient-self-supervised-speaker","title":"Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning","date":"2022-07-12","arxiv_id":"2207.05506","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-group-meiosis-contrastive","slug":"self-supervised-group-meiosis-contrastive","title":"Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition","date":"2022-07-12","arxiv_id":"2208.00877","repositories_listed":1,"syntology":null},{"url":"/paper/brain-aware-replacements-for-supervised","slug":"brain-aware-replacements-for-supervised","title":"Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease","date":"2022-07-11","arxiv_id":"2207.04574","repositories_listed":1,"syntology":null},{"url":"/paper/automating-detection-of-papilledema-in","slug":"automating-detection-of-papilledema-in","title":"Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning","date":"2022-07-10","arxiv_id":"2207.04565","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-ellipsometry-portable-acquisition-of","slug":"sparse-ellipsometry-portable-acquisition-of","title":"Sparse Ellipsometry: Portable Acquisition of Polarimetric SVBRDF and Shape with Unstructured Flash Photography","date":"2022-07-09","arxiv_id":"2207.04236","repositories_listed":1,"syntology":null},{"url":"/paper/training-robust-deep-models-for-time-series","slug":"training-robust-deep-models-for-time-series","title":"Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis","date":"2022-07-09","arxiv_id":"2207.04305","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-joint-image-transfer-and","slug":"unsupervised-joint-image-transfer-and","title":"Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks","date":"2022-07-09","arxiv_id":"2207.04325","repositories_listed":1,"syntology":null},{"url":"/paper/not-all-broken-defenses-are-equal-the-dead","slug":"not-all-broken-defenses-are-equal-the-dead","title":"How many perturbations break this model? Evaluating robustness beyond adversarial accuracy","date":"2022-07-08","arxiv_id":"2207.04129","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-from-spatio-temporal","slug":"contrastive-learning-from-spatio-temporal","title":"Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action Recognition","date":"2022-07-07","arxiv_id":"2207.03065","repositories_listed":1,"syntology":null},{"url":"/paper/harnessing-out-of-distribution-examples-via","slug":"harnessing-out-of-distribution-examples-via","title":"Harnessing Out-Of-Distribution Examples via Augmenting Content and Style","date":"2022-07-07","arxiv_id":"2207.03162","repositories_listed":1,"syntology":null},{"url":"/paper/vector-quantisation-for-robust-segmentation","slug":"vector-quantisation-for-robust-segmentation","title":"Vector Quantisation for Robust Segmentation","date":"2022-07-05","arxiv_id":"2207.01919","repositories_listed":1,"syntology":null},{"url":"/paper/stabilizing-off-policy-deep-reinforcement","slug":"stabilizing-off-policy-deep-reinforcement","title":"Stabilizing Off-Policy Deep Reinforcement Learning from Pixels","date":"2022-07-03","arxiv_id":"2207.00986","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/stabilizing-off-policy-deep-reinforcement#ran","syntology_url":"https://syntology.ai/paper/2207.00986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.00986"}},"official":{"repos":["aladoro/stabilizing-off-policy-rl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ciaug-equipping-interpolative-augmentation","slug":"ciaug-equipping-interpolative-augmentation","title":"CIAug: Equipping Interpolative Augmentation with Curriculum Learning","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generative-cross-domain-data-augmentation-for","slug":"generative-cross-domain-data-augmentation-for","title":"Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rcl-relation-contrastive-learning-for-zero","slug":"rcl-relation-contrastive-learning-for-zero","title":"RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/reler-zju-alibaba-submission-to-the-ego4d","slug":"reler-zju-alibaba-submission-to-the-ego4d","title":"ReLER@ZJU-Alibaba Submission to the Ego4D Natural Language Queries Challenge 2022","date":"2022-07-01","arxiv_id":"2207.00383","repositories_listed":1,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":16,"n_instrument":1,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":13,"n_pointer_only":4,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 3 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/reler-zju-alibaba-submission-to-the-ego4d#ran","syntology_url":"https://syntology.ai/paper/2207.00383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.00383"}},"official":{"repos":["nnnnai/ego4d_nlq_2022_1st_place_solution"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sentence-level-resampling-for-named-entity-1","slug":"sentence-level-resampling-for-named-entity-1","title":"Sentence-Level Resampling for Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-generalization-of-supervised","slug":"improving-the-generalization-of-supervised","title":"No Reason for No Supervision: Improved Generalization in Supervised Models","date":"2022-06-30","arxiv_id":"2206.15369","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":6,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"7 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improving-the-generalization-of-supervised#ran","syntology_url":"https://syntology.ai/paper/2206.15369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.15369"}},"official":null}},{"url":"/paper/insmix-towards-realistic-generative-data","slug":"insmix-towards-realistic-generative-data","title":"InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation","date":"2022-06-30","arxiv_id":"2206.15134","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-multimedia","slug":"self-supervised-learning-for-multimedia","title":"Self-Supervised Learning for Multimedia Recommendation","date":"2022-06-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-for-learning-predictive","slug":"data-augmentation-for-learning-predictive","title":"Data augmentation for learning predictive models on EEG: a systematic comparison","date":"2022-06-29","arxiv_id":"2206.14483","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/data-augmentation-for-learning-predictive#ran","syntology_url":"https://syntology.ai/paper/2206.14483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.14483"}},"official":{"repos":["eeg-augmentation-benchmark/eeg-augmentation-benchmark-2022"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/distilling-model-failures-as-directions-in","slug":"distilling-model-failures-as-directions-in","title":"Distilling Model Failures as Directions in Latent Space","date":"2022-06-29","arxiv_id":"2206.14754","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/distilling-model-failures-as-directions-in#ran","syntology_url":"https://syntology.ai/paper/2206.14754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.14754"}},"official":{"repos":["madrylab/failure-directions"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"8ff7cf7397600ee258410836930dba65c1a330e7e5b0bcf1099e7f7b7dcc799b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}