Industrial anomaly detection is an important task within computer vision with
a wide range of practical use cases. The small size of anomalous regions in
many real-world datasets necessitates processing the images at a high
resolution. This frequently poses significant challenges concerning memory
consumption during the model training and inference stages, leaving some
existing methods impractical for widespread adoption. To overcome this
challenge, we present the tiled ensemble approach, which reduces memory
consumption by dividing the input images into a grid of tiles and training a
dedicated model for each tile location. The tiled ensemble is compatible with
any existing anomaly detection model without the need for any modification of
the underlying architecture. By introducing overlapping tiles, we utilize the
benefits of traditional stacking ensembles, leading to further improvements in
anomaly detection capabilities beyond high resolution alone. We perform a
comprehensive analysis using diverse underlying architectures, including Padim,
PatchCore, FastFlow, and Reverse Distillation, on two standard anomaly
detection datasets: MVTec and VisA. Our method demonstrates a notable
improvement across setups while remaining within GPU memory constraints,
consuming only as much GPU memory as a single model needs to process a single
tile.

工业异常检测是计算机视觉中的重要任务，其实际用途广泛。为了解决处理高分辨率图像时出现的内存消耗问题，本文引入了块状集合的方法，在不修改底层架构的情况下，将输入图像划分为网格状瓦片，并为每个位置训练一个专用模型。通过引入重叠瓦片，利用传统的堆叠集合的优势，进一步提高了异常检测能力。实验证明，我们的方法在满足 GPU 内存限制的情况下，在 MVTec 和 VisA 两个标准异常检测数据集上取得了显著的改进。