Regularized Contrastive Partial Multi-view Outlier Detection
- URL: http://arxiv.org/abs/2408.07819v1
- Date: Fri, 2 Aug 2024 14:34:27 GMT
- Title: Regularized Contrastive Partial Multi-view Outlier Detection
- Authors: Yijia Wang, Qianqian Xu, Yangbangyan Jiang, Siran Dai, Qingming Huang,
- Abstract summary: We propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD)
In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency.
Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors.
- Score: 76.77036536484114
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, multi-view outlier detection (MVOD) methods have advanced significantly, aiming to identify outliers within multi-view datasets. A key point is to better detect class outliers and class-attribute outliers, which only exist in multi-view data. However, existing methods either is not able to reduce the impact of outliers when learning view-consistent information, or struggle in cases with varying neighborhood structures. Moreover, most of them do not apply to partial multi-view data in real-world scenarios. To overcome these drawbacks, we propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD). In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Specifically, we propose (1) An outlier-aware contrastive loss with a potential outlier memory bank to eliminate their bias motivated by a theoretical analysis. (2) A neighbor alignment contrastive loss to capture the view-shared local structural correlation. (3) A spreading regularization loss to prevent the model from overfitting over outliers. With the Cross-view Relation Transfer technique, we could easily impute the missing view samples based on the features of neighbors. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors under different settings.
Related papers
- VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection [5.66050466694651]
We propose Vision-Language (VL) encoders into existing anomaly detectors to leverage the semantically broad VL pre-training for improved outlier awareness.
We also propose a new scoring function that enables data- and training-free outlier supervision via textual prompts.
The resulting VL4AD model achieves competitive performance on widely used benchmark datasets.
arXiv Detail & Related papers (2024-09-25T20:12:10Z) - Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering [35.1801853090859]
Anomaly detection on graphs plays an important role in many real-world applications.
We propose an autoencoder-based clustering framework regularized by a similarity-guided contrastive loss to detect anomalous nodes.
arXiv Detail & Related papers (2024-09-15T15:41:59Z) - Hierarchical Mutual Information Analysis: Towards Multi-view Clustering
in The Wild [9.380271109354474]
This work proposes a deep MVC framework where data recovery and alignment are fused in a hierarchically consistent way to maximize the mutual information among different views.
To the best of our knowledge, this could be the first successful attempt to handle the missing and unaligned data problem separately with different learning paradigms.
arXiv Detail & Related papers (2023-10-28T06:43:57Z) - Variational Distillation for Multi-View Learning [104.17551354374821]
We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning.
Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
arXiv Detail & Related papers (2022-06-20T03:09:46Z) - Robust Contrastive Learning against Noisy Views [79.71880076439297]
We propose a new contrastive loss function that is robust against noisy views.
We show that our approach provides consistent improvements over the state-of-the-art image, video, and graph contrastive learning benchmarks.
arXiv Detail & Related papers (2022-01-12T05:24:29Z) - Revisiting Contrastive Methods for Unsupervised Learning of Visual
Representations [78.12377360145078]
Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection.
In this paper, we first study how biases in the dataset affect existing methods.
We show that current contrastive approaches work surprisingly well across: (i) object- versus scene-centric, (ii) uniform versus long-tailed and (iii) general versus domain-specific datasets.
arXiv Detail & Related papers (2021-06-10T17:59:13Z) - Solving Inefficiency of Self-supervised Representation Learning [87.30876679780532]
Existing contrastive learning methods suffer from very low learning efficiency.
Under-clustering and over-clustering problems are major obstacles to learning efficiency.
We propose a novel self-supervised learning framework using a median triplet loss.
arXiv Detail & Related papers (2021-04-18T07:47:10Z) - Homophily Outlier Detection in Non-IID Categorical Data [43.51919113927003]
This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data.
It first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation.
The learned value outlierness allows for either direct outlier detection or outlying feature selection.
arXiv Detail & Related papers (2021-03-21T23:29:33Z) - Unsupervised Noisy Tracklet Person Re-identification [100.85530419892333]
We present a novel selective tracklet learning (STL) approach that can train discriminative person re-id models from unlabelled tracklet data.
This avoids the tedious and costly process of exhaustively labelling person image/tracklet true matching pairs across camera views.
Our method is particularly more robust against arbitrary noisy data of raw tracklets therefore scalable to learning discriminative models from unconstrained tracking data.
arXiv Detail & Related papers (2021-01-16T07:31:00Z) - On Mutual Information in Contrastive Learning for Visual Representations [19.136685699971864]
unsupervised, "contrastive" learning algorithms in vision have been shown to learn representations that perform remarkably well on transfer tasks.
We show that this family of algorithms maximizes a lower bound on the mutual information between two or more "views" of an image.
We find that the choice of negative samples and views are critical to the success of these algorithms.
arXiv Detail & Related papers (2020-05-27T04:21:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.