# PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge

arXiv: https://arxiv.org/abs/2410.11591 · HTML: https://mlsanity.com/paper/2410.11591/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 26 of 26)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [MCUNet: Tiny Deep Learning on IoT Devices](https://mlsanity.com/paper/2007.10319/) (2020)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2006.16067/) (2020)
- [SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation](https://mlsanity.com/paper/2104.14812/) (2021)
- [Student-Teacher Feature Pyramid Matching for Anomaly Detection](https://mlsanity.com/paper/2103.04257/) (2021)
- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization](https://mlsanity.com/paper/2206.04325/) (2022)
- [BMAD: Benchmarks for Medical Anomaly Detection](https://mlsanity.com/paper/2306.11876/) (2023)
- [IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing](https://mlsanity.com/paper/2301.13359/) (2023)
- [MicroNet: Improving Image Recognition with Extremely Low FLOPs](https://mlsanity.com/paper/2108.05894/) (2021)
- [MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices](https://mlsanity.com/paper/1912.01106/) (2019)
- [PhiNets: a scalable backbone for low-power AI at the edge](https://mlsanity.com/paper/2110.00337/) (2021)
- [Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning](https://mlsanity.com/paper/2403.15463/) (2024)
- [tinyCLAP: Distilling Constrastive Language-Audio Pretrained Models](https://mlsanity.com/paper/2311.14517/) (2023)

## Built on by (most cited first, 16 of 16)

- [Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detection](https://mlsanity.com/paper/2503.02691/) (2025)
- [Towards Continual Visual Anomaly Detection in the Medical Domain](https://mlsanity.com/paper/2508.18013/) (2025)
- [TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection](https://mlsanity.com/paper/2603.16451/) (2026)
- [Efficient Visual Anomaly Detection at the Edge: Enabling Real-Time Industrial Inspection on Resource-Constrained Devices](https://mlsanity.com/paper/2603.20288/) (2026)
- [From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms](https://mlsanity.com/paper/2502.18328/) (2025)
- [Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression](https://mlsanity.com/paper/2505.07119/) (2025)
- [Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions](https://mlsanity.com/paper/2604.06435/) (2026)
- [Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study](https://mlsanity.com/paper/2505.07576/) (2025)
- [MoViAD: A Modular Library for Visual Anomaly Detection](https://mlsanity.com/paper/2507.12049/) (2025)
- [Explainable Visual Anomaly Detection via Concept Bottleneck Models](https://mlsanity.com/paper/2511.20088/) (2025)
- [On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturing](https://mlsanity.com/paper/2512.13497/) (2025)
- [VAD4Space: Visual Anomaly Detection for Planetary Surface Imagery](https://mlsanity.com/paper/2603.13993/) (2026)
- [AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving](https://mlsanity.com/paper/2604.15291/) (2026)
- [Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions](https://mlsanity.com/paper/2605.24251/) (2026)
- [Value-order Decomposition for Generalist Anomaly Detection](https://mlsanity.com/paper/2606.14475/) (2026)
- [LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing](https://mlsanity.com/paper/2607.01949/) (2026)

## Abstract (arXiv)

Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A key advantage of VAD is its unsupervised nature, which eliminates the need for costly and time-consuming labeled data collection. However, despite its potential for real-world applications, the literature has given limited focus to resource-efficient VAD, particularly for deployment on edge devices. This work addresses this gap by leveraging lightweight neural networks to reduce memory and computation requirements, enabling VAD deployment on resource-constrained edge devices. We benchmark the major VAD algorithms within this framework and demonstrate the feasibility of edge-based VAD using the well-known MVTec dataset. Furthermore, we introduce a novel algorithm, Partially Shared Teacher-student (PaSTe), designed to address the high resource demands of the existing Student Teacher Feature Pyramid Matching (STFPM) approach. Our results show that PaSTe decreases the inference time by 25%, while reducing the training time by 33% and peak RAM usage during training by 76%. These improvements make the VAD process significantly more efficient, laying a solid foundation for real-world deployment on edge devices.