Building effective deep neural network architectures one feature at a time
This work addresses the challenge of optimizing network width and parameter efficiency for deep learning practitioners, offering an incremental improvement over existing architecture design methods.
The paper tackles the problem of designing effective convolutional neural network architectures by introducing a bottom-up approach that starts with a single feature per layer and greedily expands width based on feature importance, resulting in networks with fewer parameters or improved accuracy compared to conventional designs.
Successful training of convolutional neural networks is often associated with sufficiently deep architectures composed of high amounts of features. These networks typically rely on a variety of regularization and pruning techniques to converge to less redundant states. We introduce a novel bottom-up approach to expand representations in fixed-depth architectures. These architectures start from just a single feature per layer and greedily increase width of individual layers to attain effective representational capacities needed for a specific task. While network growth can rely on a family of metrics, we propose a computationally efficient version based on feature time evolution and demonstrate its potency in determining feature importance and a networks' effective capacity. We demonstrate how automatically expanded architectures converge to similar topologies that benefit from lesser amount of parameters or improved accuracy and exhibit systematic correspondence in representational complexity with the specified task. In contrast to conventional design patterns with a typical monotonic increase in the amount of features with increased depth, we observe that CNNs perform better when there is more learnable parameters in intermediate, with falloffs to earlier and later layers.