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Sparse neural networks are a key factor in developing resource-efficientmachine learning applications. We propose the novel and powerful sparselearning method Adaptive Regularized Training (ART) to compress dense intosparse networks. Instead of the commonly used binary mask during training toreduce the number of model weights, we inherently shrink weights close to zeroin an iterative manner with increasing weight regularization. Our methodcompresses the pre-trained model knowledge into the weights of highestmagnitude. Therefore, we introduce a novel regularization loss namedHyperSparse that exploits the highest weights while conserving the ability ofweight exploration. Extensive experiments on CIFAR and TinyImageNet show thatour method leads to notable performance gains compared to other sparsificationmethods, especially in extremely high sparsity regimes up to 99.8 percent modelsparsity. Additional investigations provide new insights into the patterns thatare encoded in weights with high magnitudes.

HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization