A fast learning algorithm for image segmentation with max-pooling convolutional networks
Jonathan Masci, Alessandro Giusti, Dan Cireşan, Gabriel Fricout, Jürgen Schmidhuber · 2013
We present a fast algorithm for training MaxPooling Convolutional Networks to segment images. This type of network yields record-breaking performance in a variety of tasks, but is normally trained on a computationally expensive patch-by-patch basis. Our new method processes each training image in a single pass, which is vastly more efficient. We validate the approach in different scenarios and report a 1500-fold speed-up. In an application to automated steel defect detection and segmentation, we obtain excellent performance with short training times.