Atomic Compression Networks

Compressed forms of deep neural networks are essential in deploying large-scale computational models on resource-constrained devices. Contrary to analogous domains where large-scale systems are build as a hierarchical repetition of small- scale units, the current practice in Machine Learning largely relies on models with non-repetitive components. In the spirit of molecular composition with repeating atoms, we advance the state-of-the-art in model compression by proposing Atomic Compression Networks (ACNs), a novel architecture that is constructed by recursive repetition of a small set of neurons. In other words, the same neurons with the same weights are stochastically re-positioned in subsequent layers of the network. Empirical evidence suggests that ACNs achieve compression rates of up to three orders of magnitudes compared to fine-tuned fully-connected neural networks (88× to 1116× reduction) with only a fractional deterioration of classification accuracy (0.15% to 5.33%). Moreover our method can yield sub-linear model complexities and permits learning deep ACNs with less parameters than a logistic regression with no decline in classification accuracy.