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We present a fast algorithm for training MaxPooling Convolutional Networks tosegment images. This type of network yields record-breaking performance in avariety of tasks, but is normally trained on a computationally expensivepatch-by-patch basis. Our new method processes each training image in a singlepass, which is vastly more efficient.  We validate the approach in different scenarios and report a 1500-foldspeed-up. In an application to automated steel defect detection andsegmentation, we obtain excellent performance with short training times.

A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks