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To obtain a high-resolution anomaly detection map, a lot of CNN forward computations are required on the whole image in a sliding window manner. This is a time-consuming process, and it takes one hour per image.
To obtain a high-resolution anomaly detection map, a lot of CNN forward computations are required on the whole image in a sliding window manner. This is a time-consuming process, and it takes one hour per image.
Fast Dense Feature Extraction with CNNs that have Pooling or Striding Layers presented a novel approach to nearly arbitrary CNN architectures for fast execution on the whole image in a sliding window manner. Therefore, I tried to implement it in STAD.
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