  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.
