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We describe a simple and general neural network weight compression approach,in which the network parameters (weights and biases) are represented in a"latent" space, amounting to a reparameterization. This space is equipped witha learned probability model, which is used to impose an entropy penalty on theparameter representation during training, and to compress the representationusing a simple arithmetic coder after training. Classification accuracy andmodel compressibility is maximized jointly, with the bitrate--accuracytrade-off specified by a hyperparameter. We evaluate the method on the MNIST,CIFAR-10 and ImageNet classification benchmarks using six distinct modelarchitectures. Our results show that state-of-the-art model compression can beachieved in a scalable and general way without requiring complex proceduressuch as multi-stage training.

Scalable Model Compression by Entropy Penalized Reparameterization