Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection

• URL: http://arxiv.org/abs/2103.00684v1
• Date: Mon, 1 Mar 2021 01:43:04 GMT
• Title: Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection
• Authors: Tomoharu Iwata, Atsutoshi Kumagai
• Abstract summary: We propose a neural network-based meta-learning method for supervised anomaly detection. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods.
• Score: 55.888835686183995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural network-based anomaly detection methods have shown to achieve high performance. However, they require a large amount of training data for each task. We propose a neural network-based meta-learning method for supervised anomaly detection. The proposed method improves the anomaly detection performance on unseen tasks, which contains a few labeled normal and anomalous instances, by meta-training with various datasets. With a meta-learning framework, quick adaptation to each task and its effective backpropagation are important since the model is trained by the adaptation for each epoch. Our model enables them by formulating adaptation as a generalized eigenvalue problem with one-class classification; its global optimum solution is obtained, and the solver is differentiable. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods on various datasets.

Related papers

• Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly [4.387337528923525]
  Anomaly detection deals with detecting deviations from established patterns within data. We propose a novel framework for online-adaptive anomaly detection using transfer learning. Experimental results showcase a detection accuracy exceeding 0.975, outperforming the state-of-the-art ET-NET approach.
  arXiv  Detail & Related papers  (2024-06-18T15:11:44Z)
• Adaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification [10.180143197144803]
  We propose an adaptive intra-class variation contrastive learning algorithm for unsupervised Re-ID, called AdaInCV. The algorithm quantitatively evaluates the learning ability of the model for each class by considering the intra-class variations after clustering. To be more specific, two new strategies are proposed: Adaptive Sample Mining (AdaSaM) and Adaptive Outlier Filter (AdaOF)
  arXiv  Detail & Related papers  (2024-04-06T15:48:14Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Active anomaly detection based on deep one-class classification [9.904380236739398]
  We tackle two essential problems of active learning for Deep SVDD: query strategy and semi-supervised learning method. First, rather than solely identifying anomalies, our query strategy selects uncertain samples according to an adaptive boundary. Second, we apply noise contrastive estimation in training a one-class classification model to incorporate both labeled normal and abnormal data effectively.
  arXiv  Detail & Related papers  (2023-09-18T03:56:45Z)
• AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection [1.0323063834827415]
  Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Model optimization for a given dataset is a cumbersome and time consuming process. We propose Anomaly Detection Neuroevolution (AD-NEv) - a scalable multi-level optimized neuroevolution framework.
  arXiv  Detail & Related papers  (2023-05-25T21:52:38Z)
• Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection [54.76993389109327]
  Unsupervised anomaly detection aims to build models to detect unseen anomalies by only training on the normal data. We propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges. AMSL incorporates a self-supervised learning module to learn general normal patterns and an adaptive memory fusion module to learn rich feature representations.
  arXiv  Detail & Related papers  (2022-01-03T03:40:21Z)
• Simple Adaptive Projection with Pretrained Features for Anomaly Detection [0.0]
  We propose a novel adaptation framework including simple linear transformation and self-attention. Our simple adaptive projection with pretrained features(SAP2) yields a novel anomaly detection criterion.
  arXiv  Detail & Related papers  (2021-12-05T15:29:59Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Few-shot Classification via Adaptive Attention [93.06105498633492]
  We propose a novel few-shot learning method via optimizing and fast adapting the query sample representation based on very few reference samples. As demonstrated experimentally, the proposed model achieves state-of-the-art classification results on various benchmark few-shot classification and fine-grained recognition datasets.
  arXiv  Detail & Related papers  (2020-08-06T05:52:59Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.