Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios | Paper Citation Record

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios write newline

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios A simple framework for contrastive learning of visual representations

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Unresolved cited work

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This paper cites Efficient and adaptive recommendation unlearning: A guided filtering framework to erase outdated preferences.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Efficient and adaptive recommendation unlearning: A guided filtering framework to erase outdated preferences

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This paper cites Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation

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This paper cites Iterative deep structural graph contrast clustering for multiview raw data.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Iterative deep structural graph contrast clustering for multiview raw data

Reference 6

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This paper cites Cross-view topology based consistent and complementary information for deep multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cross-view topology based consistent and complementary information for deep multi-view clustering

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This paper cites Robust contrastive multi-view clustering against dual noisy correspondence.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Robust contrastive multi-view clustering against dual noisy correspondence

Reference 8

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This paper cites and Khasahmadi, A.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios and Khasahmadi, A

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Momentum contrast for unsupervised visual representation learning

Reference 10

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Unresolved cited work

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This paper cites Learning deep representations by mutual information estimation and maximization.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Learning deep representations by mutual information estimation and maximization

Reference 12

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This paper cites Exploring the role of node diversity in directed graph representation learning.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Exploring the role of node diversity in directed graph representation learning

Reference 13

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This paper cites On which nodes does gcn fail? enhancing gcn from the node perspective.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios On which nodes does gcn fail? enhancing gcn from the node perspective

Reference 14

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This paper cites T., Lv, J., and Peng, X.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios T., Lv, J., and Peng, X

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios and Dayan, P

Reference 16

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This paper cites B., and Kanagachidambaresan, G.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios B., and Kanagachidambaresan, G

Reference 17

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This paper cites Adam: A Method for Stochastic Optimization.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Adam: A Method for Stochastic Optimization

Reference 18

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This paper cites Cross-view graph matching guided anchor alignment for incomplete multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cross-view graph matching guided anchor alignment for incomplete multi-view clustering

Reference 19

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios P., Sun, Y., Sun, Q., Sun, Y., W

Reference 20

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This paper cites Consensus graph learning for multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Consensus graph learning for multi-view clustering

Reference 21

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This paper cites Efficient one-pass multi-view subspace clustering with consensus anchors.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Efficient one-pass multi-view subspace clustering with consensus anchors

Reference 22

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This paper cites One pass late fusion multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios One pass late fusion multi-view clustering

Reference 23

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Deep graph clustering via dual correlation reduction

Reference 24

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Simple contrastive graph clustering

Reference 25

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This paper cites Decoupled contrastive multi-view clustering with high-order random walks.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Decoupled contrastive multi-view clustering with high-order random walks

Reference 26

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Revisiting self-supervised heterogeneous graph learning from spectral clustering perspective

Reference 27

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Hg-adapter: Improving pre-trained heterogeneous graph neural networks with dual adapters

Reference 28

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Robust multi-view clustering with noisy correspondence

Reference 29

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Contrastive multiview coding

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios J., Lokse, S., Jenssen, R., and Kampffmeyer, M

Reference 31

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Self-supervised Learning from a Multi-view Perspective

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Representation Learning with Contrastive Predictive Coding

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios and Hinton, G

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Continual multi-view clustering

Reference 35

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Fast continual multi-view clustering with incomplete views

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios View gap matters: Cross-view topology and information decoupling for multi-view clustering

Reference 37

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Evaluate then cooperate: Shapley-based view cooperation enhancement for multi-view clustering

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Generative partial multi-view clustering with adaptive fusion and cycle consistency

Reference 39

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Incomplete multi-view clustering via graph regularized matrix factorization

Reference 40

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Y., and He, L

Reference 41

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Multi-level feature learning for contrastive multi-view clustering

Reference 42

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This paper cites Investigating and mitigating the side effects of noisy views for self-supervised clustering algorithms in practical multi-view scenarios.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Investigating and mitigating the side effects of noisy views for self-supervised clustering algorithms in practical multi-view scenarios

Reference 43

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Partially view-aligned representation learning with noise-robust contrastive loss

Reference 44

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Robust multi-view clustering with incomplete information

Reference 45

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Interpolation-based contrastive learning for few-label semi-supervised learning

Reference 46

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Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Dealmvc: Dual contrastive calibration for multi-view clustering

Reference 47

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This paper cites Cluster-guided contrastive graph clustering network.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cluster-guided contrastive graph clustering network

Reference 48

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This paper cites Z., Liu, X., and Zhu, E.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Z., Liu, X., and Zhu, E

Reference 49

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This paper cites Hyperbolic contrastive learning for cross-domain recommendation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Hyperbolic contrastive learning for cross-domain recommendation

Reference 50

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This paper cites Graphlearner: Graph node clustering with fully learnable augmentation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Graphlearner: Graph node clustering with fully learnable augmentation

Reference 51

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7e46bf3d-419a-45ed-ae09-1a48f88e8758 · outbound

This paper cites Mixed graph contrastive network for semi-supervised node classification.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Mixed graph contrastive network for semi-supervised node classification

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:50.689681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aaf40fec-ae85-48ff-b732-b2d3d90361d1 · outbound

This paper cites Darec: A disentangled alignment framework for large language model and recommender system.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Darec: A disentangled alignment framework for large language model and recommender system

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:50.436593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8540a564-519f-445a-9340-28cf9af29903 · outbound

This paper cites Dual test-time training for out-of-distribution recommender system.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Dual test-time training for out-of-distribution recommender system

Reference 54

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T13:40:46.168958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a3056988-ee64-48b7-b3da-c4fb468b3500 · outbound

This paper cites Apgl4sr: A generic framework with adaptive and personalized global collaborative information in sequential recommendation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Apgl4sr: A generic framework with adaptive and personalized global collaborative information in sequential recommendation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:50.216236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 90418e67-1c52-47d0-b460-79e1d4b6e0c2 · outbound

This paper cites Dataset regeneration for sequential recommendation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Dataset regeneration for sequential recommendation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:50.032797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 66dbc22f-fbc3-4159-aa09-cb5d5031adfd · outbound

This paper cites Gzoo: Black-box node injection attack on graph neural networks via zeroth-order optimization.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Gzoo: Black-box node injection attack on graph neural networks via zeroth-order optimization

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:49.791620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3c9e1a3e-4a36-406c-8705-a93c3793226f · outbound

This paper cites Dshield: Defending against backdoor attacks on graph neural networks via discrepancy learning.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Dshield: Defending against backdoor attacks on graph neural networks via discrepancy learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:49.524095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6fe5e819-1e39-417e-ba4a-e0bbdc7aeaf0 · outbound

This paper cites Sparse low-rank multi-view subspace clustering with consensus anchors and unified bipartite graph.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Sparse low-rank multi-view subspace clustering with consensus anchors and unified bipartite graph

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:49.313831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3e70332c-dfba-4a8b-9c12-130168ff4186 · outbound

This paper cites How to construct corresponding anchors for incomplete multiview clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios How to construct corresponding anchors for incomplete multiview clustering

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:49.112508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c3475c05-d7b2-49bc-a48b-59a1a839b187 · outbound

This paper cites Towards resource-friendly, extensible and stable incomplete multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Towards resource-friendly, extensible and stable incomplete multi-view clustering

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.889333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3ac7ed0a-79f1-4599-818c-586e06908fdc · outbound

This paper cites Cross-domain recommendation via user interest alignment.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cross-domain recommendation via user interest alignment

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.682733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e757ca9-4878-489f-a7d4-e89ef2298e7b · outbound

This paper cites Cross-domain recommendation via progressive structural alignment.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cross-domain recommendation via progressive structural alignment

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.506732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0a77508c-9a42-494a-821e-b6182b5b8281 · outbound

This paper cites Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.371152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4735574e-23c1-4dc4-9f26-98c4463895f6 · outbound

This paper cites Multiple kernel clustering with neighbor-kernel subspace segmentation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Multiple kernel clustering with neighbor-kernel subspace segmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.197475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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