FastADnet: Fast Anomaly Detection via Core-Feature Centered Cluster Reconstruction Network

Industrial anomaly detection plays a critical role in computer vision, driving advancements across various sectors. In recent works in this field, there are trade-offs between computational complexity and anomaly detection accuracy for effective deployment. To achieve both reduced latency and improved accuracy, we propose a simple reconstruction network that learns local clusters centered at core-features derived from PatchCore. By leveraging these features that represent local data distributions and their adjacent patch-features, our network effectively enhances anomaly detection along with reduced inference time. Specifically, the proposed reconstruction network is designed to reconstruct core-features from their neighbors, effectively acting as a denoiser for Neighbor Noise. So neighbor noise is defined as the distance between a core-feature and its adjacent patch-features, which can be regarded as a noise added to core-faeture. By reducing this noise, our network creates tightly clustered reconstruction of core-features and their neighbors, excluding reconstructions of anomaly features. Evaluated on MVTecAD and MPDD datasets, our approach outperforms existing methods in both inference efficiency and accuracy. The code is available at https://github.com/Nadaeyeob/FastADnet.