Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data | Paper Citation Record

Observation 02c2287f-94f6-42e0-8b68-1492ffe1e3c0 · outbound

This paper cites Microsoft coco: Common objects in context.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Microsoft coco: Common objects in context

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.159418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4e3e39ff-6db0-4ab1-8a5f-3c83e4cc1803 · outbound

This paper cites an unresolved cited work.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:21:30.151643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b7c5468-3e3e-4652-a63f-afae4bf09a1c · outbound

This paper cites Improving transfer learning for movie trailer genre classification using a dual image and video transformer.Information Processing & Management, 60(3):103343, 2023.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Improving transfer learning for movie trailer genre classification using a dual image and video transformer.Information Processing & Management, 60(3):103343, 2023

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.143934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4bba786e-c6bc-4ccd-ad59-6de40b39e3a8 · outbound

This paper cites Neural legal judgment prediction in English.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Neural legal judgment prediction in English

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.136105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d8d3dbe-004d-411f-98f9-e596f15e128c · outbound

This paper cites Efficient few-shot learning for multi-label classification of scientific documents with many classes.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Efficient few-shot learning for multi-label classification of scientific documents with many classes

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.128295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6fe791d2-b3d3-4c43-b87f-08d256f4ceab · outbound

This paper cites Toward purifying defect feature for multilabel sewer defect classification.IEEE Transactions on Instrumentation and Measurement, 72: 1–11, 2023.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Toward purifying defect feature for multilabel sewer defect classification.IEEE Transactions on Instrumentation and Measurement, 72: 1–11, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.119996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 533aa675-c320-4bda-a1e8-2e3105bd73bc · outbound

This paper cites Defecttr: End-to-end defect detection for sewage networks using a transformer.Construction and Building Materials, 325: 126584, 2022.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Defecttr: End-to-end defect detection for sewage networks using a transformer.Construction and Building Materials, 325: 126584, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.111540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a5d12b91-b8c1-4d45-a992-9491bc0f26c8 · outbound

This paper cites Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.103052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 946dec32-707f-40ea-8eea-3cac2e0e97d6 · outbound

This paper cites Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.094620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1aefb8e4-c4c1-42b3-8a2d-bdb9688e9ad5 · outbound

This paper cites Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet.PLoS medicine, 15(11): e1002699, 2018.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet.PLoS medicine, 15(11): e1002699, 2018

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.086240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0634ce86-ced3-4f7b-a61f-7dba9a47a38c · outbound

This paper cites Sewer-ml: A multi-label sewer defect classification dataset and benchmark.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Sewer-ml: A multi-label sewer defect classification dataset and benchmark

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.077897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4583d4c-20a7-4593-afdb-5f45f083d520 · outbound

This paper cites Focal loss for dense object detection.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Focal loss for dense object detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.069923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 22ea2bd0-4eb7-405c-92b5-843408790483 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Class-balanced loss based on effective number of samples

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.061902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 99407d5d-0e5b-4597-988c-c2bad0c3139b · outbound

This paper cites On active learning in multi-label classification.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data On active learning in multi-label classification

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.053673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ff6a1003-8f21-4fc8-9180-1456daa34de1 · outbound

This paper cites Comprehensive comparative study of multi-label classification methods.Expert Systems with Applications, 203:117215, 2022.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Comprehensive comparative study of multi-label classification methods.Expert Systems with Applications, 203:117215, 2022

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:21:21.605704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e1f2638-fae2-41ec-a1fc-f920347020a9 · outbound

This paper cites an unresolved cited work.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:21:30.039700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation facd6797-41b9-4835-9333-6dd7308890c2 · outbound

This paper cites Learning a deep convnet for multi-label classification with partial labels.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Learning a deep convnet for multi-label classification with partial labels

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.031733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e486dab9-5617-4ec2-a79a-d1fd95a0455c · outbound

This paper cites Binary relevance for multi-label learning: an overview.Frontiers of Computer Science, 12:191–202, 2018.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Binary relevance for multi-label learning: an overview.Frontiers of Computer Science, 12:191–202, 2018

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.022679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 64b49876-35ad-48b0-b425-3767d2151e6e · outbound

This paper cites Multi-label learning with stronger consistency guarantees.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Multi-label learning with stronger consistency guarantees

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.014456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4650760f-b0ad-44fd-9fc3-9321230d5d43 · outbound

This paper cites Multilabel classification via calibrated label ranking.Machine learning, 73:133–153, 2008.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Multilabel classification via calibrated label ranking.Machine learning, 73:133–153, 2008

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:30.006122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation df74494c-8c92-4612-b663-3d203bf1acdc · outbound

This paper cites Classifier chains for multi-label classification.Machine learning, 85:333–359, 2011.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Classifier chains for multi-label classification.Machine learning, 85:333–359, 2011

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.997757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 24e0505c-9fb2-49e5-8164-63c8ea018897 · outbound

This paper cites Multi-label learning from single positive labels.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Multi-label learning from single positive labels

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.989295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a0b7f49-cf68-4074-89c9-c48aef376685 · outbound

This paper cites Deep long-tailed learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10795–10816, 2023.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Deep long-tailed learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10795–10816, 2023

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T10:21:22.198041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fa0e010-cb89-4141-bcc5-c11807987e6b · outbound

This paper cites When noisy labels meet long tail dilemmas: A representation calibration method.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data When noisy labels meet long tail dilemmas: A representation calibration method

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.975548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 61b60b62-502b-4e30-84b5-683267b4b95c · outbound

This paper cites Long tail multi-label learning.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Long tail multi-label learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.967793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ea102062-83b0-4ef7-af0b-eb78ad7067af · outbound

This paper cites Distribution-balanced loss for multi-label classification in long-tailed datasets.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Distribution-balanced loss for multi-label classification in long-tailed datasets

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.959645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 425555d1-0fe6-4eb3-85b7-9d496620f40b · outbound

This paper cites Edcloc: a prediction model for mrna subcellular localization using improved focal loss to address multi-label class imbalance.BMC genomics, 25(1):1252, 2024.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Edcloc: a prediction model for mrna subcellular localization using improved focal loss to address multi-label class imbalance.BMC genomics, 25(1):1252, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.950756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dc22a61a-1176-410e-8929-93d2cff81584 · outbound

This paper cites Asymmetric loss for multi-label classification.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Asymmetric loss for multi-label classification

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.941933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3582a3eb-dd45-4517-a7b9-af51ad91712e · outbound

This paper cites Semi-supervised multi-label learning with balanced binary angular margin loss.Advances in Neural Information Processing Systems, 37: 97884–97906, 2024.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Semi-supervised multi-label learning with balanced binary angular margin loss.Advances in Neural Information Processing Systems, 37: 97884–97906, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.933476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation adda393b-d64c-4de6-988f-e843f3e8ad86 · outbound

This paper cites Long-tail learning via logit adjustment.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Long-tail learning via logit adjustment

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.887297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1d6b7b35-9126-4a38-aca5-e75b1adaf47a · outbound

This paper cites an unresolved cited work.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:21:29.487085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 21913f61-c6da-400d-b83a-dcf5e073c931 · outbound

This paper cites Revisiting deep learning models for tabular data.Advances in Neural Information Processing Systems, 34:18932–18943, 2021.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Revisiting deep learning models for tabular data.Advances in Neural Information Processing Systems, 34:18932–18943, 2021

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T10:21:22.964993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4ed11be-94b2-4763-8a10-f1b873b76461 · outbound

This paper cites The emerging trends of multi-label learning.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data The emerging trends of multi-label learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.266792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bad8503c-42c3-4d10-896d-f505d49c4f07 · outbound

This paper cites Multi-label local awareness and global co-occurrence priori learning improve chest x-ray classification.Multimedia Systems, 30(3):132, 2024.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Multi-label local awareness and global co-occurrence priori learning improve chest x-ray classification.Multimedia Systems, 30(3):132, 2024

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:29.087896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d2e0cd46-a3d3-4c5a-bd2e-2315b35e7096 · outbound

This paper cites Improving multi-label recognition using class co-occurrence probabilities.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Improving multi-label recognition using class co-occurrence probabilities

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:28.958883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6725b05d-74df-4a6b-87e7-acd3dd6351d8 · outbound

This paper cites Multi-label out-of-distribution detection via exploiting sparsity and co-occurrence of labels.Image and Vision Computing, 126:104548, 2022.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Multi-label out-of-distribution detection via exploiting sparsity and co-occurrence of labels.Image and Vision Computing, 126:104548, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:28.848420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 945bf2c3-82a9-44a6-9d49-37d04eebd256 · outbound

This paper cites Evidential mixture machines: Deciphering multi-label correlations for active learning sensitivity.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Evidential mixture machines: Deciphering multi-label correlations for active learning sensitivity

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:28.724557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2995fa7a-87a2-40b4-a161-6a3aa8e97f1b · outbound

This paper cites In pursuit of causal label correlations for multi-label image recognition.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data In pursuit of causal label correlations for multi-label image recognition

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:28.575367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4373118a-cd35-46ff-b280-8b8c93f189a1 · outbound

This paper cites Ml-decoder: Scalable and versatile classification head.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Ml-decoder: Scalable and versatile classification head

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:28.242118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bcf95e29-bb8b-425b-92b1-af2672803766 · outbound

This paper cites Coocnet: a novel approach to multi-label text classification with improved label co-occurrence modeling.Applied Intelligence, 54(17):8702–8718, 2024.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Coocnet: a novel approach to multi-label text classification with improved label co-occurrence modeling.Applied Intelligence, 54(17):8702–8718, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:28.115066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 47206fa8-3aab-43c0-af86-c64e2cf7f5ad · outbound

This paper cites Dao, Ethan Zhao, Dinh Phung, and Jianfei Cai.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Dao, Ethan Zhao, Dinh Phung, and Jianfei Cai

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:27.979539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fce6352f-73ef-418e-8b13-e5d55868b0aa · outbound

This paper cites A review of methods for imbalanced multi-label classification.Pattern Recognition, 118:107965, 2021.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data A review of methods for imbalanced multi-label classification.Pattern Recognition, 118:107965, 2021

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:27.845499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9ab37d61-3019-4b0d-9d21-4093158582e0 · outbound

This paper cites Multi-label learning with weak label.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Multi-label learning with weak label

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:27.721640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3b130fea-1237-4172-8ef4-8a8fa87141dc · outbound

This paper cites Using deep learning for image-based plant disease detection.Frontiers in plant science, 7:215232, 2016.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Using deep learning for image-based plant disease detection.Frontiers in plant science, 7:215232, 2016

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:27.584155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a72357b9-e41f-4563-8109-18afce07b350 · outbound

This paper cites Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:27.455827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1e279963-081e-4e1e-9d99-58e69c1b74d0 · outbound

This paper cites Multi-label classification by exploiting local positive and negative pairwise label correlation.Neurocomputing, 257:164–174, 2017.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Multi-label classification by exploiting local positive and negative pairwise label correlation.Neurocomputing, 257:164–174, 2017

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:27.300636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bdedb18d-a644-4d50-835a-cc4abee78244 · outbound

This paper cites V ogel and.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data V ogel and

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:27.136743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3b0f6e7b-f254-4db7-8193-3be7087a9a31 · outbound

This paper cites Multi-label classification of chest x-ray abnormalities using transfer learning techniques.Journal of Personalized Medicine, 13(10): 1426, 2023.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Multi-label classification of chest x-ray abnormalities using transfer learning techniques.Journal of Personalized Medicine, 13(10): 1426, 2023

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:26.976381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ac1dd185-01bf-4db2-b017-96eaeabe75cf · outbound

This paper cites Tresnet: High performance gpu-dedicated architecture.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Tresnet: High performance gpu-dedicated architecture

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:26.826501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b28f3a7-b585-47a4-b769-5a36dc52b87a · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data An image is worth 16x16 words: Transformers for image recognition at scale

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:26.693114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2029b150-15e9-4328-bb13-c71bf9413c32 · outbound

This paper cites Maxvit: Multi-axis vision transformer.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Maxvit: Multi-axis vision transformer

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:26.512062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 64efc594-5b0a-476a-b91d-b78465700014 · outbound

This paper cites Decoupled weight decay regularization.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Decoupled weight decay regularization

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:26.261960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 466d81c7-a9aa-46c1-945d-683de09d2f45 · outbound

This paper cites SGDR: Stochastic gradient descent with warm restarts.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data SGDR: Stochastic gradient descent with warm restarts

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:26.077152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 726fbc73-5c99-44d9-9710-c027f6a86b1c · outbound

This paper cites Multi-scale hybrid vision transformer and sinkhorn tokenizer for sewer defect classification.Automation in Construction, 144: 104614, 2022.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Multi-scale hybrid vision transformer and sinkhorn tokenizer for sewer defect classification.Automation in Construction, 144: 104614, 2022

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:25.931223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bae66c8c-9e30-4476-bd9b-f6dd51c3fcca · outbound

This paper cites Any-Class.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Any-Class

Reference 55

Resolution: verified exact; raw_fallback, observed 2026-08-07T10:21:25.784509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4dc8c550-589b-46b6-bb76-9198e4ce41b4 · outbound

This paper cites an unresolved cited work.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:21:29.702814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a27a945a-a9be-4a00-bad4-b0fb775df618 · outbound

This paper cites an unresolved cited work.

Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:21:28.409223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers