High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble

Core Concepts

The author presents a tiled ensemble approach to high-resolution anomaly detection, reducing memory consumption by training dedicated models for each tile location. This method enhances anomaly detection capabilities beyond high resolution alone.

Abstract

The content introduces a novel approach, the tiled ensemble, for industrial anomaly detection in high-resolution images. By dividing images into tiles and training separate models for each tile location, the method reduces memory consumption while improving anomaly detection performance. The approach is flexible, compatible with various architectures, and showcases significant improvements in detecting small anomalies across different datasets. The paper highlights the importance of efficient anomaly detection in computer vision applications and addresses challenges related to memory consumption during model training and inference stages. By leveraging overlapping tiles and stacking ensembles, the tiled ensemble method offers enhanced performance without compromising GPU memory constraints. The study includes detailed experiments on established datasets to demonstrate the effectiveness of the proposed approach. Overall, the tiled ensemble method provides a practical solution for detecting and localizing anomalies in high-resolution images while ensuring efficient use of GPU memory. The results indicate superior performance compared to traditional methods, especially in scenarios with small anomalies that require increased resolution for accurate detection.

Stats

Our method demonstrates a notable improvement across setups while remaining within GPU memory constraints. The configuration yields 9 overlapping tiles labeled T0,0 through T2,2. A single model with an input size of 256×256 pixels serves as a baseline. The final predicted anomaly map maintains the same dimensions as the input image. Each setup is trained on every category with every run repeated 3 times using a different random seed.

Quotes

"The tiled ensemble approach addresses this by employing a separate model for each tile location." "By introducing overlapping tiles, this approach also takes advantage of the performance improvements associated with traditional stacking ensemble methods." "The results presented in this paper demonstrate the feasibility of applying existing or next-generation anomaly detection models within high-resolution imagery."

Key Insights Distilled From

Divide and Conquer

by Blaž... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.04932.pdf

Deeper Inquiries

How can batched inference mitigate latency overhead in tiled ensemble approaches

Batched inference can mitigate latency overhead in tiled ensemble approaches by optimizing the utilization of GPU resources. By processing multiple tiles simultaneously in batches, the model can make more efficient use of available computational power. This approach reduces the time needed to transfer individual models to and from the GPU, leading to a more streamlined process and ultimately decreasing latency during inference.

What are potential limitations of tiling input data for accurate logical anomaly detection

One potential limitation of tiling input data for accurate logical anomaly detection is the challenge it poses in capturing global context effectively. Tiling divides the image into smaller sections, which may hinder the model's ability to understand relationships and patterns that span across different parts of the image. As anomalies often exhibit complex spatial dependencies, tiling could restrict the model's capacity to detect anomalies that rely on broader contextual information rather than localized features within a single tile.

How does choosing a single layer strategically impact anomaly detection performance compared to an ensemble of multiple layers

Strategically choosing a single layer can impact anomaly detection performance by optimizing feature extraction for specific characteristics present in anomaly data. In contrast, an ensemble of multiple layers may introduce redundancy or conflicting information that hampers accurate anomaly detection. By selecting a layer tailored to capture relevant features effectively, anomaly detection models can focus on extracting key information critical for distinguishing anomalies from normal data, potentially enhancing overall performance and reducing noise in predictions.