Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection

• URL: http://arxiv.org/abs/2308.04944v1
• Date: Wed, 9 Aug 2023 13:19:28 GMT
• Title: Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection
• Abstract summary: Anomaly detection (AD) in images, identifying significant deviations from normality, is a critical issue in computer vision. This paper introduces a novel approach to dimensionality reduction for AD using pre-trained convolutional neural network (CNN) that incorporate EfficientNet models. Our results indicate that the proposed method surpasses both Principal Component Analysis (PCA) and Negated Principal Component Analysis (NPCA) in terms of detection accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection (AD) in images, identifying significant deviations from normality, is a critical issue in computer vision. This paper introduces a novel approach to dimensionality reduction for AD using pre-trained convolutional neural network (CNN) that incorporate EfficientNet models. We investigate the importance of component selection and propose two types of tree search approaches, both employing a greedy strategy, for optimal eigencomponent selection. Our study conducts three main experiments to evaluate the effectiveness of our approach. The first experiment explores the influence of test set performance on component choice, the second experiment examines the performance when we train on one anomaly type and evaluate on all other types, and the third experiment investigates the impact of using a minimum number of images for training and selecting them based on anomaly types. Our approach aims to find the optimal subset of components that deliver the highest performance score, instead of focusing solely on the proportion of variance explained by each component and also understand the components behaviour in different settings. Our results indicate that the proposed method surpasses both Principal Component Analysis (PCA) and Negated Principal Component Analysis (NPCA) in terms of detection accuracy, even when using fewer components. Thus, our approach provides a promising alternative to conventional dimensionality reduction techniques in AD, and holds potential to enhance the efficiency and effectiveness of AD systems.

Related papers

• Evaluating ML-based Intrusion Detection Systems: The Illusion of Model Efficacy [0.0]
  Intrusion Detection has been revolutionized due to the integration of Machine Learning.<n>In this paper, we go beyond solely algorithmic enhancements and metric adjustments in ML-based Network Intrusion Detection Systems.
  arXiv  Detail & Related papers  (2026-09-02T11:39:02Z)
• Objective-Induced Bias and Search Dynamics in Multiobjective Unsupervised Feature Selection [2.2282508171588806]
  We study the choice of evaluation objective, the direction of subset-size regularisation, and the initialisation strategy.<n>We show that objective design is central to effective multiobjective unsupervised feature selection.
  arXiv  Detail & Related papers  (2026-05-20T15:14:00Z)
• Towards Size-invariant Salient Object Detection: A Generic Evaluation and Optimization Approach [118.75896764188424]
  We present a novel perspective to expose the inherent size sensitivity of existing widely used Salient Object Detection metrics.<n>To address this challenge, a generic Size-Invariant Evaluation (SIEva) framework is proposed.<n>We further develop a dedicated optimization framework (SIOpt), which adheres to the size-invariant principle and significantly enhances the detection of salient objects across a broad range of sizes.
  arXiv  Detail & Related papers  (2025-09-19T04:12:14Z)
• Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology [1.8434042562191815]
  Physics-Informed Kolmogorov Networks (PIKANs) are gaining attention as an effective counterpart to the original multilayer perceptron-based Physics-Informed Networks (PINNs)
  arXiv  Detail & Related papers  (2025-04-10T01:37:18Z)
• Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
  Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance. We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection [52.716143424856185]
  We propose LiMA (Less input is More faithful for Attribution), which reformulates the attribution of important regions as an optimization problem for submodular subset selection. LiMA identifies both the most and least important samples while ensuring an optimal attribution boundary that minimizes errors. Our method also outperforms the greedy search in attribution efficiency, being 1.6 times faster.
  arXiv  Detail & Related papers  (2025-04-01T06:58:15Z)
• Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspective [0.0]
  We propose an E2E differentiable decision-focused learning scheme for particle tracking. We show that differentiable variations of discrete assignment operations allows for efficient network optimization. We argue that E2E differentiability provides, besides the general availability of gradient information, an important tool for robust particle tracking to mitigate prediction instabilities.
  arXiv  Detail & Related papers  (2024-07-18T11:42:58Z)
• Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection [0.18648070031379424]
  Biomarker identification with feature selection methods can be addressed as a multi-objective problem with trade-offs between predictive ability and parsimony in the number of features. We propose DOSA-MO, a novel multi-objective optimization wrapper algorithm that learns how the original estimation, its variance, and the feature set size of the solutions predict the overestimation.
  arXiv  Detail & Related papers  (2023-12-27T16:13:14Z)
• Class Anchor Margin Loss for Content-Based Image Retrieval [97.81742911657497]
  We propose a novel repeller-attractor loss that falls in the metric learning paradigm, yet directly optimize for the L2 metric without the need of generating pairs. We evaluate the proposed objective in the context of few-shot and full-set training on the CBIR task, by using both convolutional and transformer architectures.
  arXiv  Detail & Related papers  (2023-06-01T12:53:10Z)
• Efficient Human-in-the-loop System for Guiding DNNs Attention [25.501443892795614]
  We propose an efficient human-in-the-loop system to interactively direct the attention of classifiers to the regions specified by users. Previous approaches for attention guidance require the preparation of pixel-level annotations and are not designed as interactive systems.
  arXiv  Detail & Related papers  (2022-06-13T09:04:32Z)
• R(Det)^2: Randomized Decision Routing for Object Detection [64.48369663018376]
  We propose a novel approach to combine decision trees and deep neural networks in an end-to-end learning manner for object detection. To facilitate effective learning, we propose randomized decision routing with node selective and associative losses. We name this approach as the randomized decision routing for object detection, abbreviated as R(Det)$2$.
  arXiv  Detail & Related papers  (2022-04-02T07:54:58Z)
• Understanding Interlocking Dynamics of Cooperative Rationalization [90.6863969334526]
  Selective rationalization explains the prediction of complex neural networks by finding a small subset of the input that is sufficient to predict the neural model output. We reveal a major problem with such cooperative rationalization paradigm -- model interlocking. We propose a new rationalization framework, called A2R, which introduces a third component into the architecture, a predictor driven by soft attention as opposed to selection.
  arXiv  Detail & Related papers  (2021-10-26T17:39:18Z)
• Reenvisioning Collaborative Filtering vs Matrix Factorization [65.74881520196762]
  Collaborative filtering models based on matrix factorization and learned similarities using Artificial Neural Networks (ANNs) have gained significant attention in recent years. Announcement of ANNs within the recommendation ecosystem has been recently questioned, raising several comparisons in terms of efficiency and effectiveness. We show the potential these techniques may have on beyond-accuracy evaluation while analyzing effect on complementary evaluation dimensions.
  arXiv  Detail & Related papers  (2021-07-28T16:29:38Z)
• Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection [4.249341912358848]
  This work is to perform a systematic assessment of the HD computing framework for the detection of epileptic seizures. We test two previously implemented features as well as several novel approaches with HD computing on epileptic seizure detection.
  arXiv  Detail & Related papers  (2021-05-03T15:11:08Z)
• Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach [27.975266406080152]
  In this paper, we design a suite of unsupervised classification methods based on experimental design approaches. We aim to select the subsets of events which minimize different measures of mean estimation error. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes.
  arXiv  Detail & Related papers  (2021-02-11T11:38:15Z)
• SADet: Learning An Efficient and Accurate Pedestrian Detector [68.66857832440897]
  This paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector. It forms a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-07-26T12:32:38Z)
• Towards High Performance Human Keypoint Detection [87.1034745775229]
  We find that context information plays an important role in reasoning human body configuration and invisible keypoints. Inspired by this, we propose a cascaded context mixer ( CCM) which efficiently integrates spatial and channel context information. To maximize CCM's representation capability, we develop a hard-negative person detection mining strategy and a joint-training strategy. We present several sub-pixel refinement techniques for postprocessing keypoint predictions to improve detection accuracy.
  arXiv  Detail & Related papers  (2020-02-03T02:24:51Z)

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.