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Successful training of convolutional neural networks is often associated withsufficiently deep architectures composed of high amounts of features. Thesenetworks typically rely on a variety of regularization and pruning techniquesto converge to less redundant states. We introduce a novel bottom-up approachto expand representations in fixed-depth architectures. These architecturesstart from just a single feature per layer and greedily increase width ofindividual layers to attain effective representational capacities needed for aspecific task. While network growth can rely on a family of metrics, we proposea computationally efficient version based on feature time evolution anddemonstrate its potency in determining feature importance and a networks'effective capacity. We demonstrate how automatically expanded architecturesconverge to similar topologies that benefit from lesser amount of parameters orimproved accuracy and exhibit systematic correspondence in representationalcomplexity with the specified task. In contrast to conventional design patternswith a typical monotonic increase in the amount of features with increaseddepth, we observe that CNNs perform better when there is more learnableparameters in intermediate, with falloffs to earlier and later layers.

Building effective deep neural network architectures one feature at a time