Interpretable Maximum Margin Deep Anomaly Detection

• URL: http://arxiv.org/abs/2603.07073v1
• Date: Sat, 07 Mar 2026 07:06:45 GMT
• Title: Interpretable Maximum Margin Deep Anomaly Detection
• Abstract summary: Anomaly detection is a crucial machine-learning task with wide-ranging applications.<n>We propose Maximum Interpretable Margin Deep Anomaly Detection (IMD-AD)
• Score: 10.25312830872628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection is a crucial machine-learning task with wide-ranging applications. Deep Support Vector Data Description (Deep SVDD) is a prominent deep one-class method, but it is vulnerable to hypersphere collapse, often relies on heuristic choices for hypersphere parameters, and provides limited interpretability. To address these issues, we propose Interpretable Maximum Margin Deep Anomaly Detection (IMD-AD), which leverages a small set of labeled anomalies and a maximum margin objective to stabilize training and improve discrimination. It is inherently resilient to hypersphere collapse. Furthermore, we prove an equivalence between hypersphere parameters and the network's final-layer weights, which allows the center and radius to be learned end-to-end as part of the model and yields intrinsic interpretability and visualizable outputs. We further develop an efficient training algorithm that jointly optimizes representation, margin, and final-layer parameters. Extensive experiments and ablation studies on image and tabular benchmarks demonstrate that IMD-AD empirically improves detection performance over several state-of-the-art baselines while providing interpretable decision diagnostics.

Related papers

• Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection [9.367612782346205]
  We propose DLM-SVDD, a large-margin novelty framework that jointly learns convolutional features and an explicit kernel-based decision boundary.<n>We show consistent performance improvements over the baseline and strong overall performance compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-07-24T11:30:03Z)
• Diagnosing and Mitigating Thinking Collapse in On-Policy Self-Distillation [21.932079347631632]
  On-Policy Self-Distillation (OPSD) has emerged as a crucial paradigm for enhancing and aligning Large Language Models (LLMs)<n>OPSD paradoxically degrades downstream performance in complex reasoning tasks.<n>We propose textbf Dual-Perspective OPSD (AD-OPSD), a robust control framework that dynamically moderates the self-distillation objective.
  arXiv  Detail & Related papers  (2026-07-12T15:24:25Z)
• Rethinking Evaluation of Infrared Small Target Detection [105.59753496831739]
  This paper introduces a hybrid-level metric incorporating pixel- and target-level performance, proposing a systematic error analysis method, and emphasizing the importance of cross-dataset evaluation.<n>An open-source toolkit has be released to facilitate standardized benchmarking.
  arXiv  Detail & Related papers  (2025-09-21T02:45:07Z)
• 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)
• ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model [51.83639270669481]
  Unsupervised anomaly detection in hyperspectral images (HSI) aims to detect unknown targets from backgrounds.<n>HSI studies are hindered by steep computational costs due to the high-dimensional property of HSI and dense sampling-based training paradigm.<n>We propose an Asymmetrical Consensus State Space Model (ACMamba) to significantly reduce computational costs without compromising accuracy.
  arXiv  Detail & Related papers  (2025-04-16T05:33:42Z)
• Nearshore Underwater Target Detection Meets UAV-borne Hyperspectral Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and Benchmark Dataset [6.832892497880372]
  UAV-borne hyperspectral remote sensing has emerged as a promising approach for underwater target detection (UTD)<n>It is hindered by spectral distortions in nearshore environments, which compromise the accuracy of traditional hyperspectral UTD methods.<n>We propose a novel framework that integrates contrastive learning with a self-paced learning paradigm for robust HUTD in nearshore regions.
  arXiv  Detail & Related papers  (2025-02-20T12:25:30Z)
• Spectrum-oriented Point-supervised Saliency Detector for Hyperspectral Images [13.79887292039637]
  We introduce point supervision into Hyperspectral salient object detection (HSOD)<n>We incorporate Spectral Saliency, derived from conventional HSOD methods, as a pivotal spectral representation within the framework.<n>We propose a novel pipeline, specifically designed for HSIs, to generate pseudo-labels, effectively mitigating the performance decline associated with point supervision strategy.
  arXiv  Detail & Related papers  (2024-12-24T02:52:43Z)
• Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification [0.0]
  We introduce a novel deep operator network (DeepONet) framework that incorporates generalised variational inference (GVI) By incorporating Bayesian neural networks as the building blocks for the branch and trunk networks, our framework endows DeepONet with uncertainty quantification. We demonstrate that modifying the variational objective function yields superior results in terms of minimising the mean squared error.
  arXiv  Detail & Related papers  (2024-08-01T16:22:03Z)
• HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding [9.32185717565188]
  This study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding. The proposed framework, HyperDID, incorporates the Environmental Feature Module (EFM) and Categorical Feature Module (CFM) to extract intrinsic features. Experimental results across three commonly used datasets validate the effectiveness of HyperDID in improving hyperspectral image classification performance.
  arXiv  Detail & Related papers  (2023-11-25T02:05:10Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization [48.5614138038673]
  We propose an efficient and robust bandit-based algorithm called Sub-Sampling (SS) in the scenario of hyper parameter search evaluation. We also develop a novel hyper parameter optimization algorithm called BOSS. Empirical studies validate our theoretical arguments of SS and demonstrate the superior performance of BOSS on a number of applications.
  arXiv  Detail & Related papers  (2020-07-11T03:15:21Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

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.