Anomalies, Representations, and Self-Supervision

• URL: http://arxiv.org/abs/2301.04660v2
• Date: Wed, 7 Aug 2024 15:07:41 GMT
• Title: Anomalies, Representations, and Self-Supervision
• Authors: Barry M. Dillon, Luigi Favaro, Friedrich Feiden, Tanmoy Modak, Tilman Plehn,
• Abstract summary: We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC 2021. The AnomalyCLR technique is data-driven and uses augmentations of the background data to mimic non-Standard-Model events in a model-agnostic way.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR technique is data-driven and uses augmentations of the background data to mimic non-Standard-Model events in a model-agnostic way. It uses a permutation-invariant Transformer Encoder architecture to map the objects measured in a collider event to the representation space, where the data augmentations define a representation space which is sensitive to potential anomalous features. An AutoEncoder trained on background representations then computes anomaly scores for a variety of signals in the representation space. With AnomalyCLR we find significant improvements on performance metrics for all signals when compared to the raw data baseline.

Related papers

• Trajectory Anomaly Detection with Language Models [21.401931052512595]
  This paper presents a novel approach for trajectory anomaly detection using an autoregressive causal-attention model, termed LM-TAD. By treating trajectories as sequences of tokens, our model learns the probability distributions over trajectories, enabling the identification of anomalous locations with high precision. Our experiments demonstrate the effectiveness of LM-TAD on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-09-18T17:33:31Z)
• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• 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)
• Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning [11.245813423781415]
  We devise novel data-driven supervision for data by introducing a characteristic -- scale -- as data labels. Scales serve as labels attached to transformed representations, thus offering ample labeled data for neural network training. This paper further proposes a scale learning-based anomaly detection method.
  arXiv  Detail & Related papers  (2023-05-25T14:48:00Z)
• Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection [15.991784541576788]
  Existing approaches, both video and segment-level label oriented, mainly focus on extracting representations for anomaly data. We propose an Uncertainty Regulated Dual Memory Units (UR-DMU) model to learn both the representations of normal data and discriminative features of abnormal data. Our method outperforms the state-of-the-art methods by a sizable margin.
  arXiv  Detail & Related papers  (2023-02-10T10:39:40Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Generative Anomaly Detection for Time Series Datasets [1.7954335118363964]
  Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. We propose a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score.
  arXiv  Detail & Related papers  (2022-06-28T17:08:47Z)
• Object-centric and memory-guided normality reconstruction for video anomaly detection [56.64792194894702]
  This paper addresses anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality modeling strategy. Our model learns object-centric normal patterns without seeing anomalous samples during training.
  arXiv  Detail & Related papers  (2022-03-07T19:28:39Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• Categorical anomaly detection in heterogeneous data using minimum description length clustering [3.871148938060281]
  We propose a meta-algorithm for enhancing any MDL-based anomaly detection model to deal with heterogeneous data. Our experimental results show that using a discrete mixture model provides competitive performance relative to two previous anomaly detection algorithms.
  arXiv  Detail & Related papers  (2020-06-14T14:48:37Z)

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.