RangeAD: Fast On-Model Anomaly Detection

Luca Hinkamp; Simon Klüttermann; Emmanuel Müller

cs.LGMar 18, 2026

RangeAD: Fast On-Model Anomaly Detection

Authors: Luca Hinkamp, Simon Klüttermann, Emmanuel Müller

Organizations: TU Dortmund University, Dortmund, Germany · Research Center Trustworthy Data Science and Security, UA Ruhr, Germany

Abstract

In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts. Typically, this is implemented by running a separate AD model alongside the primary model. However, this separation ignores the fact that the primary model already encodes substantial information about the target distribution. In this paper, we introduce On-Model AD, a setting for anomaly detection that explicitly leverages access to a related machine learning model. Within this setting, we propose RangeAD, an algorithm that utilizes neuron-wise output ranges derived from the primary model. RangeAD achieves superior performance even on high-dimensional tasks while incurring substantially lower inference costs. Our results demonstrate the potential of the On-Model AD setting as a practical framework for efficient anomaly detection.

Figures & tables

Panel of Figure 1 — Figure 1: Depiction of our proposed methodology . (a) represents the neural network, from which the sample activations per neuron are depicted in (b). From these distributions - to be considered normal - interval borders are derived. The activations produced for a new test input are then checked per neuron to see whether it falls inside the corresponding interval in (c). For every test sample $x_{i}$ the amount of feature-outputs lying outside of the corresponding interval indicates the anomaly grade of $x_{i}$ .

Figure 2: AUC-ROC of different anomaly detection methods over 12 tabular datasets. Our method is run three times with models having hidden-layer neuron counts of 100, 500, and 1000. It reaches the highest AUC-ROC on average compared to all competitors.

Figure 3: AUC-ROC and inference time of different anomaly detection methods averaged over 12 tabular datasets. Our method is run three times with models with hidden layer neuron counts of 100, 500, 1000. It achieves the highest detection performance while providing the fastest inference runtime of all competitors. We provide the same analysis on the training time in the supplementary material.

Dataset	in, 0.01	after, 0.01	in, 0.001	after, 0.001	IF	AE	DeepSVDD
INet + Ambivision	0.9893	0.9923	0.9945	0.9937	0.9985	0.9472	0.8830
INet + SpaceImages	0.9625	0.9094	0.9592	0.909	0.4977	0.1864	0.6757
INet + both	0.971	0.9355	0.9703	0.9357	0.6597	0.4266	0.6833
UrbanSound [ 43 ]	0.8804	-	0.9142	-	0.6939	0.8002	0.7927

Table 1: AUC-ROC of vision and time series tasks. "in" means in the network blocks, e.g. after GELU and Convolution-Layers; "after" means after the network blocks, e.g. Transformer-Block (vision-only). 0.01 and 0.001 is the quantile used. AE and DeepSVDD were trained with 10% of the INet testset for memory reasons. Our method reaches the most reliable performance across high-dimensional datasets.

Dataset	in, 0.01	after, 0.01	in, 0.001	after, 0.001	IF	AE	DeepSVDD
Inet + Ambivision	0.0151	0.0161	0.0131	0.0129	0.8197	240.9355	133.9441
Inet + SpaceImages	0.0131	0.012	0.0123	0.0129	0.8438	238.3285	138.8146
Inet + both	0.0129	0.0126	0.0129	0.0136	0.8560	251.041	141.8705
UrbanSound [ 43 ]	0.0138	-	0.0103	-	0.0711	0.8833	0.5184

Table 2: Inference time in seconds of vision and time series tasks. "in" means in the network blocks, e.g. after GELU and Convolution-Layers; "after" means after the network blocks, e.g. Transformer-Block (vision-only). 0.01 and 0.001 are the quantiles used. AE and DeepSVDD were trained with 10% of the INet testset for memory reasons. Our method requires multiple orders of magnitude less runtime as it is not using an additional model.

Figure 4: When using differently sized hidden layers in our neural networks, AUC ROC and model performance are correlated . The results represent the average of 5 runs with a standard deviation over the 12 tabular datasets.

Panel of Figure 5 — Figure 5: Ablation study results.

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Beyond Normal References: Discriminative Few-Shot Anomaly Detection

Huan Wang, Jun Shen, Jun Yan, Guansong Pang

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Few-Shot Anomaly Detection Few-Shot Learning Anomaly Detection

Open paper AskPDF arXiv

This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomalous examples are available as references during inference. Existing FSAD methods rely on normal-only references through normality matching, ignoring the discriminative clues in anomalous references, while directly fitting both references can overfit to the seen anomalies. We introduce IDEAL, an intrinsic deviation learning framework that leverages both reference types to learn intrinsic deviation patterns characterizing generalizable abnormality as deviations from normality. IDEAL decomposes the learning process into two novel components: 1) a Normal Variation Eraser to suppress nuisance normal variations that may lead to noisy deviations from normality, thereby highlighting anomaly-relevant deviation representations; 2) an Intrinsic Deviation Encoder to decompose these denoised deviation representations into intrinsic deviation vectors capturing the most discriminative orthogonal deviation directions. At inference, IDEAL scores query-to-normal deviations preserved after projection onto the learned intrinsic deviation vectors, enabling generalization for both seen and unseen anomalies. Extensive experiments on eight real-world datasets show that IDEAL generalizes effectively to unseen anomalies and consistently outperforms existing state-of-the-art FSAD methods. Code and data are available at https://github.com/mala-lab/IDEAL.

Abstract

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