The example networks presented so far were designed manually to highlight different features of the Neural Abstraction Pyramid architecture. While the manually designed networks are relatively easy to interpret, their utility is limited by the low network complexity. Only relatively few features can be designed manually. If multiple layers of abstraction are needed, the design complexity explodes with height, as the number of different feature arrays and the number of potential weights per feature increase exponentially.
KeywordsIndependent Component Analysis Unsupervised Learning Edge Feature Handwritten Digit Sparse Feature
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