{"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/image-classification/papers/31","list_of":"/task/image-classification","task":"Image Classification","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":31,"pages_in_order":105,"rows_per_page":100,"rows":[3001,3100],"of":10488,"counts":{"archive_papers_tagged":10488,"with_a_code_link":4702,"where_syntology_ran_a_sample":1392,"not_listed_spam_title":0,"listed":10488,"listed_where_code_ran":1392,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1164,"every_run_a_failure_of_syntologys_instrument":228,"listed_with_a_run_with_no_instrument_failure":1164,"listed_every_run_a_failure_of_syntologys_instrument":228,"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/image-classification","prev":"/task/image-classification/papers/30","next":"/task/image-classification/papers/32","papers":[{"url":"/paper/clcnet-rethinking-of-ensemble-modeling-with","slug":"clcnet-rethinking-of-ensemble-modeling-with","title":"CLCNet: Rethinking of Ensemble Modeling with Classification Confidence Network","date":"2022-05-19","arxiv_id":"2205.09612","repositories_listed":1,"syntology":null},{"url":"/paper/masked-image-modeling-with-denoising-contrast","slug":"masked-image-modeling-with-denoising-contrast","title":"Masked Image Modeling with Denoising Contrast","date":"2022-05-19","arxiv_id":"2205.09616","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/masked-image-modeling-with-denoising-contrast#ran","syntology_url":"https://syntology.ai/paper/2205.09616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.09616"}},"official":{"repos":["tencentarc/conmim"],"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/cross-domain-few-shot-meta-learning-using","slug":"cross-domain-few-shot-meta-learning-using","title":"Feature Extractor Stacking for Cross-domain Few-shot Learning","date":"2022-05-12","arxiv_id":"2205.05831","repositories_listed":1,"syntology":null},{"url":"/paper/elodi-ensemble-logit-difference-inhibition","slug":"elodi-ensemble-logit-difference-inhibition","title":"ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training","date":"2022-05-12","arxiv_id":"2205.06265","repositories_listed":1,"syntology":null},{"url":"/paper/feedback-gradient-descent-efficient-and","slug":"feedback-gradient-descent-efficient-and","title":"Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs","date":"2022-05-12","arxiv_id":"2205.08385","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/feedback-gradient-descent-efficient-and#ran","syntology_url":"https://syntology.ai/paper/2205.08385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.08385"}},"official":{"repos":["bokveizen/Feedback-Gradient-Descent"],"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/leveraging-uncertainty-for-deep-interpretable","slug":"leveraging-uncertainty-for-deep-interpretable","title":"Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images","date":"2022-05-12","arxiv_id":"2205.05841","repositories_listed":1,"syntology":null},{"url":"/paper/hyperspectral-image-classification-with-5","slug":"hyperspectral-image-classification-with-5","title":"Hyperspectral Image Classification With Contrastive Graph Convolutional Network","date":"2022-05-11","arxiv_id":"2205.11237","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-logo-recognition-and-retrieval","slug":"multi-label-logo-recognition-and-retrieval","title":"Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features","date":"2022-05-11","arxiv_id":"2205.05419","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-deep-learning-methods-in-medical","slug":"explainable-deep-learning-methods-in-medical","title":"Explainable Deep Learning Methods in Medical Image Classification: A Survey","date":"2022-05-10","arxiv_id":"2205.04766","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-image-classification-benchmarks-are","slug":"few-shot-image-classification-benchmarks-are","title":"Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling","date":"2022-05-10","arxiv_id":"2205.05155","repositories_listed":1,"syntology":null},{"url":"/paper/smoothnets-optimizing-cnn-architecture-design","slug":"smoothnets-optimizing-cnn-architecture-design","title":"SmoothNets: Optimizing CNN architecture design for differentially private deep learning","date":"2022-05-09","arxiv_id":"2205.04095","repositories_listed":1,"syntology":null},{"url":"/paper/when-does-dough-become-a-bagel-analyzing-the","slug":"when-does-dough-become-a-bagel-analyzing-the","title":"When does dough become a bagel? Analyzing the remaining mistakes on ImageNet","date":"2022-05-09","arxiv_id":"2205.04596","repositories_listed":1,"syntology":null},{"url":"/paper/preservation-of-high-frequency-content-for","slug":"preservation-of-high-frequency-content-for","title":"Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification","date":"2022-05-08","arxiv_id":"2205.03898","repositories_listed":1,"syntology":null},{"url":"/paper/zero-and-r2d2-a-large-scale-chinese-cross","slug":"zero-and-r2d2-a-large-scale-chinese-cross","title":"CCMB: A Large-scale Chinese Cross-modal Benchmark","date":"2022-05-08","arxiv_id":"2205.03860","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/zero-and-r2d2-a-large-scale-chinese-cross#ran","syntology_url":"https://syntology.ai/paper/2205.03860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.03860"}},"official":{"repos":["yuxie11/R2D2"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/comparison-knowledge-translation-for","slug":"comparison-knowledge-translation-for","title":"Comparison Knowledge Translation for Generalizable Image Classification","date":"2022-05-07","arxiv_id":"2205.03633","repositories_listed":1,"syntology":null},{"url":"/paper/all-grains-one-scheme-agos-learning-multi","slug":"all-grains-one-scheme-agos-learning-multi","title":"All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification","date":"2022-05-06","arxiv_id":"2205.03371","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-and-explaining-the-frequency","slug":"investigating-and-explaining-the-frequency","title":"Investigating and Explaining the Frequency Bias in Image Classification","date":"2022-05-06","arxiv_id":"2205.03154","repositories_listed":1,"syntology":null},{"url":"/paper/image-classification-with-small-datasets","slug":"image-classification-with-small-datasets","title":"Image Classification With Small Datasets: Overview and Benchmark","date":"2022-05-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/immiscible-color-flows-in-optimal-transport","slug":"immiscible-color-flows-in-optimal-transport","title":"Immiscible Color Flows in Optimal Transport Networks for Image Classification","date":"2022-05-04","arxiv_id":"2205.02938","repositories_listed":1,"syntology":null},{"url":"/paper/deepgravilens-a-multi-modal-architecture-for","slug":"deepgravilens-a-multi-modal-architecture-for","title":"DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data","date":"2022-05-02","arxiv_id":"2205.00701","repositories_listed":1,"syntology":null},{"url":"/paper/augmented-balanced-image-dataset-generator","slug":"augmented-balanced-image-dataset-generator","title":"Augmented Balanced Image Dataset Generator Using AugStatic Library","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/augstatic-a-light-weight-image-augmentation","slug":"augstatic-a-light-weight-image-augmentation","title":"AugStatic - A Light-Weight Image Augmentation Library","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-estimation-of-transformer","slug":"uncertainty-estimation-of-transformer","title":"Uncertainty Estimation of Transformer Predictions for Misclassification Detection","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/elucidating-meta-structures-of-noisy-labels","slug":"elucidating-meta-structures-of-noisy-labels","title":"Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks","date":"2022-04-30","arxiv_id":"2205.00160","repositories_listed":1,"syntology":null},{"url":"/paper/engineering-flexible-machine-learning-systems","slug":"engineering-flexible-machine-learning-systems","title":"Engineering flexible machine learning systems by traversing functionally-invariant paths","date":"2022-04-30","arxiv_id":"2205.00334","repositories_listed":1,"syntology":null},{"url":"/paper/operational-adaptation-of-dnn-classifiers","slug":"operational-adaptation-of-dnn-classifiers","title":"DIRA: Dynamic Domain Incremental Regularised Adaptation","date":"2022-04-30","arxiv_id":"2205.00147","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-split-for-automatic-bias","slug":"learning-to-split-for-automatic-bias","title":"Learning to Split for Automatic Bias Detection","date":"2022-04-28","arxiv_id":"2204.13749","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-communication-an-information","slug":"semantic-communication-an-information","title":"Semantic Information Recovery in Wireless Networks","date":"2022-04-28","arxiv_id":"2204.13366","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-attention-mechanisms-for-medical","slug":"a-survey-on-attention-mechanisms-for-medical","title":"A survey on attention mechanisms for medical applications: are we moving towards better algorithms?","date":"2022-04-26","arxiv_id":"2204.12406","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-split-fusion-transformer","slug":"adaptive-split-fusion-transformer","title":"Adaptive Split-Fusion Transformer","date":"2022-04-26","arxiv_id":"2204.12196","repositories_listed":1,"syntology":null},{"url":"/paper/causal-transportability-for-visual","slug":"causal-transportability-for-visual","title":"Causal Transportability for Visual Recognition","date":"2022-04-26","arxiv_id":"2204.12363","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/causal-transportability-for-visual#ran","syntology_url":"https://syntology.ai/paper/2204.12363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12363"}},"official":{"repos":["cvlab-columbia/ct4recognition"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/one-shot-federated-learning-without-server","slug":"one-shot-federated-learning-without-server","title":"One-shot Federated Learning without Server-side Training","date":"2022-04-26","arxiv_id":"2204.12493","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-hybrid-activation-function-for-deep","slug":"adaptive-hybrid-activation-function-for-deep","title":"Adaptive hybrid activation function for deep neural networks","date":"2022-04-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/do-users-benefit-from-interpretable-vision-a-1","slug":"do-users-benefit-from-interpretable-vision-a-1","title":"Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset","date":"2022-04-25","arxiv_id":"2204.11642","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-audio-strikes-back-boosting","slug":"end-to-end-audio-strikes-back-boosting","title":"End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network","date":"2022-04-25","arxiv_id":"2204.11479","repositories_listed":1,"syntology":null},{"url":"/paper/surpassing-the-human-accuracy-detecting","slug":"surpassing-the-human-accuracy-detecting","title":"Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning","date":"2022-04-25","arxiv_id":"2204.11433","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-sonar-image","slug":"self-supervised-learning-for-sonar-image","title":"Self-supervised Learning for Sonar Image Classification","date":"2022-04-20","arxiv_id":"2204.09323","repositories_listed":1,"syntology":null},{"url":"/paper/an-extendable-efficient-and-effective","slug":"an-extendable-efficient-and-effective","title":"An Extendable, Efficient and Effective Transformer-based Object Detector","date":"2022-04-17","arxiv_id":"2204.07962","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/an-extendable-efficient-and-effective#ran","syntology_url":"https://syntology.ai/paper/2204.07962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07962"}},"official":{"repos":["naver-ai/vidt"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["community","official"]}}},{"url":"/paper/continual-hippocampus-segmentation-with","slug":"continual-hippocampus-segmentation-with","title":"Continual Hippocampus Segmentation with Transformers","date":"2022-04-17","arxiv_id":"2204.08043","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-signatures","slug":"learning-with-signatures","title":"Learning with Signatures","date":"2022-04-17","arxiv_id":"2204.07953","repositories_listed":1,"syntology":null},{"url":"/paper/towards-lightweight-transformer-via-group","slug":"towards-lightweight-transformer-via-group","title":"Towards Lightweight Transformer via Group-wise Transformation for Vision-and-Language Tasks","date":"2022-04-16","arxiv_id":"2204.07780","repositories_listed":1,"syntology":null},{"url":"/paper/pushing-the-limits-of-simple-pipelines-for","slug":"pushing-the-limits-of-simple-pipelines-for","title":"Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference","date":"2022-04-15","arxiv_id":"2204.07305","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":8,"n_instrument":6,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":4,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/pushing-the-limits-of-simple-pipelines-for#ran","syntology_url":"https://syntology.ai/paper/2204.07305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07305"}},"official":{"repos":["hushell/pmf_cvpr22"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/vitol-vision-transformer-for-weakly","slug":"vitol-vision-transformer-for-weakly","title":"ViTOL: Vision Transformer for Weakly Supervised Object Localization","date":"2022-04-14","arxiv_id":"2204.06772","repositories_listed":1,"syntology":null},{"url":"/paper/viscuit-visual-auditor-for-bias-in-cnn-image","slug":"viscuit-visual-auditor-for-bias-in-cnn-image","title":"VisCUIT: Visual Auditor for Bias in CNN Image Classifier","date":"2022-04-12","arxiv_id":"2204.05899","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-state-of-the-art-with","slug":"machine-learning-state-of-the-art-with","title":"Machine Learning State-of-the-Art with Uncertainties","date":"2022-04-11","arxiv_id":"2204.05173","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/machine-learning-state-of-the-art-with#ran","syntology_url":"https://syntology.ai/paper/2204.05173","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.05173"}},"official":{"repos":["psteinb/sota_on_uncertainties"],"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/no-token-left-behind-explainability-aided","slug":"no-token-left-behind-explainability-aided","title":"No Token Left Behind: Explainability-Aided Image Classification and Generation","date":"2022-04-11","arxiv_id":"2204.04908","repositories_listed":1,"syntology":null},{"url":"/paper/regularization-based-pruning-of-irrelevant","slug":"regularization-based-pruning-of-irrelevant","title":"Regularization-based Pruning of Irrelevant Weights in Deep Neural Architectures","date":"2022-04-11","arxiv_id":"2204.04977","repositories_listed":1,"syntology":null},{"url":"/paper/superpixelgridcut-superpixelgridmean-and","slug":"superpixelgridcut-superpixelgridmean-and","title":"SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation","date":"2022-04-11","arxiv_id":"2204.08458","repositories_listed":1,"syntology":null},{"url":"/paper/dilemma-self-supervised-shape-and-texture","slug":"dilemma-self-supervised-shape-and-texture","title":"Representation Learning by Detecting Incorrect Location Embeddings","date":"2022-04-10","arxiv_id":"2204.04788","repositories_listed":1,"syntology":null},{"url":"/paper/generative-adversarial-networks-for-image","slug":"generative-adversarial-networks-for-image","title":"Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review","date":"2022-04-10","arxiv_id":"2204.04707","repositories_listed":1,"syntology":null},{"url":"/paper/neural-networks-embrace-learned-diversity","slug":"neural-networks-embrace-learned-diversity","title":"Neuronal diversity can improve machine learning for physics and beyond","date":"2022-04-09","arxiv_id":"2204.04348","repositories_listed":1,"syntology":null},{"url":"/paper/total-variation-optimization-layers-for","slug":"total-variation-optimization-layers-for","title":"Total Variation Optimization Layers for Computer Vision","date":"2022-04-07","arxiv_id":"2204.03643","repositories_listed":1,"syntology":null},{"url":"/paper/unified-contrastive-learning-in-image-text","slug":"unified-contrastive-learning-in-image-text","title":"Unified Contrastive Learning in Image-Text-Label Space","date":"2022-04-07","arxiv_id":"2204.03610","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/unified-contrastive-learning-in-image-text#ran","syntology_url":"https://syntology.ai/paper/2204.03610","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03610"}},"official":{"repos":["microsoft/unicl"],"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/fine-grained-predicates-learning-for-scene","slug":"fine-grained-predicates-learning-for-scene","title":"Fine-Grained Predicates Learning for Scene Graph Generation","date":"2022-04-06","arxiv_id":"2204.02597","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":3,"n_ran_checked":5,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":10,"phrase":"9 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fine-grained-predicates-learning-for-scene#ran","syntology_url":"https://syntology.ai/paper/2204.02597","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02597"}},"official":{"repos":["xinyulyu/fgpl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/metaaudio-a-few-shot-audio-classification","slug":"metaaudio-a-few-shot-audio-classification","title":"MetaAudio: A Few-Shot Audio Classification Benchmark","date":"2022-04-05","arxiv_id":"2204.02121","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/metaaudio-a-few-shot-audio-classification#ran","syntology_url":"https://syntology.ai/paper/2204.02121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02121"}},"official":{"repos":["cheggan/metaaudio-a-few-shot-audio-classification-benchmark"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/real-time-hyperspectral-imaging-in-hardware","slug":"real-time-hyperspectral-imaging-in-hardware","title":"Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders","date":"2022-04-05","arxiv_id":"2204.02084","repositories_listed":1,"syntology":null},{"url":"/paper/batchformerv2-exploring-sample-relationships","slug":"batchformerv2-exploring-sample-relationships","title":"BatchFormerV2: Exploring Sample Relationships for Dense Representation Learning","date":"2022-04-04","arxiv_id":"2204.01254","repositories_listed":1,"syntology":null},{"url":"/paper/co-teaching-for-unsupervised-domain","slug":"co-teaching-for-unsupervised-domain","title":"Co-Teaching for Unsupervised Domain Adaptation and Expansion","date":"2022-04-04","arxiv_id":"2204.01210","repositories_listed":1,"syntology":null},{"url":"/paper/multimae-multi-modal-multi-task-masked","slug":"multimae-multi-modal-multi-task-masked","title":"MultiMAE: Multi-modal Multi-task Masked Autoencoders","date":"2022-04-04","arxiv_id":"2204.01678","repositories_listed":1,"syntology":null},{"url":"/paper/improving-vision-transformers-by-revisiting","slug":"improving-vision-transformers-by-revisiting","title":"Improving Vision Transformers by Revisiting High-frequency Components","date":"2022-04-03","arxiv_id":"2204.00993","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/improving-vision-transformers-by-revisiting#ran","syntology_url":"https://syntology.ai/paper/2204.00993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00993"}},"official":{"repos":["jiawangbai/HAT"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/chordal-sparsity-for-lipschitz-constant","slug":"chordal-sparsity-for-lipschitz-constant","title":"Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks","date":"2022-04-02","arxiv_id":"2204.00846","repositories_listed":1,"syntology":null},{"url":"/paper/triplenet-a-low-computing-power-platform-of","slug":"triplenet-a-low-computing-power-platform-of","title":"Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification","date":"2022-04-02","arxiv_id":"2204.00943","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/triplenet-a-low-computing-power-platform-of#ran","syntology_url":"https://syntology.ai/paper/2204.00943","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00943"}},"official":{"repos":["RuiyangJu/TripleNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/proper-reuse-of-image-classification-features","slug":"proper-reuse-of-image-classification-features","title":"Proper Reuse of Image Classification Features Improves Object Detection","date":"2022-04-01","arxiv_id":"2204.00484","repositories_listed":1,"syntology":null},{"url":"/paper/deep-hyperspectral-unmixing-using-transformer","slug":"deep-hyperspectral-unmixing-using-transformer","title":"Deep Hyperspectral Unmixing using Transformer Network","date":"2022-03-31","arxiv_id":"2203.17076","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-patch-label-inference","slug":"weakly-supervised-patch-label-inference","title":"Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild","date":"2022-03-31","arxiv_id":"2203.16782","repositories_listed":1,"syntology":null},{"url":"/paper/a-fuzzy-distance-based-ensemble-of-deep","slug":"a-fuzzy-distance-based-ensemble-of-deep","title":"A fuzzy distance-based ensemble of deep models for cervical cancer detection","date":"2022-03-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/amstertime-a-visual-place-recognition","slug":"amstertime-a-visual-place-recognition","title":"AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift","date":"2022-03-30","arxiv_id":"2203.16291","repositories_listed":1,"syntology":null},{"url":"/paper/fair-contrastive-learning-for-facial","slug":"fair-contrastive-learning-for-facial","title":"Fair Contrastive Learning for Facial Attribute Classification","date":"2022-03-30","arxiv_id":"2203.16209","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/fair-contrastive-learning-for-facial#ran","syntology_url":"https://syntology.ai/paper/2203.16209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16209"}},"official":{"repos":["sungho-coolg/fscl"],"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/chex-channel-exploration-for-cnn-model","slug":"chex-channel-exploration-for-cnn-model","title":"CHEX: CHannel EXploration for CNN Model Compression","date":"2022-03-29","arxiv_id":"2203.15794","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chex-channel-exploration-for-cnn-model#ran","syntology_url":"https://syntology.ai/paper/2203.15794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15794"}},"official":null}},{"url":"/paper/cnn-filter-db-an-empirical-investigation-of","slug":"cnn-filter-db-an-empirical-investigation-of","title":"CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters","date":"2022-03-29","arxiv_id":"2203.15331","repositories_listed":1,"syntology":null},{"url":"/paper/nested-collaborative-learning-for-long-tailed","slug":"nested-collaborative-learning-for-long-tailed","title":"Nested Collaborative Learning for Long-Tailed Visual Recognition","date":"2022-03-29","arxiv_id":"2203.15359","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/nested-collaborative-learning-for-long-tailed#ran","syntology_url":"https://syntology.ai/paper/2203.15359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15359"}},"official":{"repos":["bazinga699/ncl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/vggin-net-deep-transfer-network-for","slug":"vggin-net-deep-transfer-network-for","title":"VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset","date":"2022-03-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-approach-for-detecting-normal-covid","slug":"a-novel-approach-for-detecting-normal-covid","title":"A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-Scans","date":"2022-03-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-prompt-for-open-vocabulary-object","slug":"learning-to-prompt-for-open-vocabulary-object","title":"Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model","date":"2022-03-28","arxiv_id":"2203.14940","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 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) · 1 unverified","sample_list":"/paper/learning-to-prompt-for-open-vocabulary-object#ran","syntology_url":"https://syntology.ai/paper/2203.14940","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14940"}},"official":{"repos":["dyabel/detpro"],"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/wsebp-a-novel-width-depth-synchronous","slug":"wsebp-a-novel-width-depth-synchronous","title":"WSEBP: A Novel Width-depth Synchronous Extension-based Basis Pursuit Algorithm for Multi-Layer Convolutional Sparse Coding","date":"2022-03-28","arxiv_id":"2203.14856","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-robustify-black-box-ml-models-a-zeroth-1","slug":"how-to-robustify-black-box-ml-models-a-zeroth-1","title":"How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective","date":"2022-03-27","arxiv_id":"2203.14195","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/how-to-robustify-black-box-ml-models-a-zeroth-1#ran","syntology_url":"https://syntology.ai/paper/2203.14195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14195"}},"official":{"repos":["damon-demon/black-box-defense"],"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/image-quality-assessment-for-machine-learning","slug":"image-quality-assessment-for-machine-learning","title":"Image quality assessment for machine learning tasks using meta-reinforcement learning","date":"2022-03-27","arxiv_id":"2203.14258","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-mining-with-scene-text-for-fine","slug":"knowledge-mining-with-scene-text-for-fine","title":"Knowledge Mining with Scene Text for Fine-Grained Recognition","date":"2022-03-27","arxiv_id":"2203.14215","repositories_listed":1,"syntology":null},{"url":"/paper/does-monocular-depth-estimation-provide","slug":"does-monocular-depth-estimation-provide","title":"On the Viability of Monocular Depth Pre-training for Semantic Segmentation","date":"2022-03-26","arxiv_id":"2203.13987","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-contrastive-distillation","slug":"uncertainty-aware-contrastive-distillation","title":"Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation","date":"2022-03-26","arxiv_id":"2203.14098","repositories_listed":1,"syntology":null},{"url":"/paper/a-stitch-in-time-saves-nine-a-train-time","slug":"a-stitch-in-time-saves-nine-a-train-time","title":"A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration","date":"2022-03-25","arxiv_id":"2203.13834","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 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) · 3 unverified","sample_list":"/paper/a-stitch-in-time-saves-nine-a-train-time#ran","syntology_url":"https://syntology.ai/paper/2203.13834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13834"}},"official":{"repos":["mdca-loss/mdca-calibration"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/moving-window-regression-a-novel-approach-to","slug":"moving-window-regression-a-novel-approach-to","title":"Moving Window Regression: A Novel Approach to Ordinal Regression","date":"2022-03-24","arxiv_id":"2203.13122","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/moving-window-regression-a-novel-approach-to#ran","syntology_url":"https://syntology.ai/paper/2203.13122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13122"}},"official":{"repos":["nhshin-mcl/mwr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/on-exploiting-layerwise-gradient-statistics","slug":"on-exploiting-layerwise-gradient-statistics","title":"A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize Range","date":"2022-03-24","arxiv_id":"2203.13273","repositories_listed":1,"syntology":null},{"url":"/paper/multidimensional-belief-quantification-for","slug":"multidimensional-belief-quantification-for","title":"Multidimensional Belief Quantification for Label-Efficient Meta-Learning","date":"2022-03-23","arxiv_id":"2203.12768","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":5,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":9,"phrase":"6 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/multidimensional-belief-quantification-for#ran","syntology_url":"https://syntology.ai/paper/2203.12768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12768"}},"official":{"repos":["pandeydeep9/units-ml-cvpr-22"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/semisupervised-cross-scale-graph-prototypical","slug":"semisupervised-cross-scale-graph-prototypical","title":"Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification","date":"2022-03-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/u-boost-nas-utilization-boosted-1","slug":"u-boost-nas-utilization-boosted-1","title":"U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search","date":"2022-03-23","arxiv_id":"2203.12412","repositories_listed":1,"syntology":null},{"url":"/paper/feddc-federated-learning-with-non-iid-data","slug":"feddc-federated-learning-with-non-iid-data","title":"FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction","date":"2022-03-22","arxiv_id":"2203.11751","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/feddc-federated-learning-with-non-iid-data#ran","syntology_url":"https://syntology.ai/paper/2203.11751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11751"}},"official":{"repos":["gaoliang13/feddc"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/generative-modeling-helps-weak-supervision","slug":"generative-modeling-helps-weak-supervision","title":"Generative Modeling Helps Weak Supervision (and Vice Versa)","date":"2022-03-22","arxiv_id":"2203.12023","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":8,"n_ran_checked":10,"n_instrument":2,"n_unverified":5,"n_honours":1,"n_violates":1,"n_no_contract":8,"n_pointer_only":1,"phrase":"12 ran (of which 8 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 1 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/generative-modeling-helps-weak-supervision#ran","syntology_url":"https://syntology.ai/paper/2203.12023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12023"}},"official":{"repos":["benbo/wsgan-paper"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":8,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/improving-generalization-in-federated","slug":"improving-generalization-in-federated","title":"Improving Generalization in Federated Learning by Seeking Flat Minima","date":"2022-03-22","arxiv_id":"2203.11834","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":0,"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/improving-generalization-in-federated#ran","syntology_url":"https://syntology.ai/paper/2203.11834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11834"}},"official":{"repos":["debcaldarola/fedsam"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-patch-to-cluster-attention-in-vision","slug":"learning-patch-to-cluster-attention-in-vision","title":"PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers","date":"2022-03-22","arxiv_id":"2203.11987","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":0,"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/learning-patch-to-cluster-attention-in-vision#ran","syntology_url":"https://syntology.ai/paper/2203.11987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11987"}},"official":{"repos":["ivmcl/pacavit"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-source-domain-adaptation-based-on","slug":"multi-source-domain-adaptation-based-on","title":"Feature Distribution Matching for Federated Domain Generalization","date":"2022-03-22","arxiv_id":"2203.11635","repositories_listed":1,"syntology":null},{"url":"/paper/on-robust-classification-using-contractive","slug":"on-robust-classification-using-contractive","title":"Robust Classification using Contractive Hamiltonian Neural ODEs","date":"2022-03-22","arxiv_id":"2203.11805","repositories_listed":1,"syntology":null},{"url":"/paper/semi-targeted-model-poisoning-attack-on","slug":"semi-targeted-model-poisoning-attack-on","title":"Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis","date":"2022-03-22","arxiv_id":"2203.11633","repositories_listed":1,"syntology":null},{"url":"/paper/generating-fast-and-slow-scene-decomposition","slug":"generating-fast-and-slow-scene-decomposition","title":"Test-time Adaptation with Slot-Centric Models","date":"2022-03-21","arxiv_id":"2203.11194","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generating-fast-and-slow-scene-decomposition#ran","syntology_url":"https://syntology.ai/paper/2203.11194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11194"}},"official":{"repos":["mihirp1998/Slot-TTA"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/clip-on-wheels-zero-shot-object-navigation-as","slug":"clip-on-wheels-zero-shot-object-navigation-as","title":"CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation","date":"2022-03-20","arxiv_id":"2203.10421","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/clip-on-wheels-zero-shot-object-navigation-as#ran","syntology_url":"https://syntology.ai/paper/2203.10421","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10421"}},"official":{"repos":["real-stanford/cow"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/do-deep-networks-transfer-invariances-across-1","slug":"do-deep-networks-transfer-invariances-across-1","title":"Do Deep Networks Transfer Invariances Across Classes?","date":"2022-03-18","arxiv_id":"2203.09739","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/do-deep-networks-transfer-invariances-across-1#ran","syntology_url":"https://syntology.ai/paper/2203.09739","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09739"}},"official":{"repos":["allanyangzhou/generative-invariance-transfer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/identifying-transients-in-the-dark-energy","slug":"identifying-transients-in-the-dark-energy","title":"Identifying Transients in the Dark Energy Survey using Convolutional Neural Networks","date":"2022-03-18","arxiv_id":"2203.09908","repositories_listed":1,"syntology":null},{"url":"/paper/data-domain-aware-and-task-aware-pre-training","slug":"data-domain-aware-and-task-aware-pre-training","title":"DATA: Domain-Aware and Task-Aware Self-supervised Learning","date":"2022-03-17","arxiv_id":"2203.09041","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 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) · 1 unverified","sample_list":"/paper/data-domain-aware-and-task-aware-pre-training#ran","syntology_url":"https://syntology.ai/paper/2203.09041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09041"}},"official":{"repos":["gaia-vision/gaia-ssl"],"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/do-we-really-need-a-learnable-classifier-at","slug":"do-we-really-need-a-learnable-classifier-at","title":"Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?","date":"2022-03-17","arxiv_id":"2203.09081","repositories_listed":1,"syntology":null},{"url":"/paper/docxclassifier-high-performance-explainable","slug":"docxclassifier-high-performance-explainable","title":"DocXClassifier: High Performance Explainable Deep Network for Document Image Classification","date":"2022-03-17","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"8735556a82656ff2a13dc5ae421fe0ab1b2eaefd810694cf10023c658b6b9e6e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}