Proximal Mean-field for Neural Network Quantization
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Compressing large neural networks by quantizing the parameters, while maintaining the performance is often highly desirable due to the reduced memory and time complexity. In this work, we formulate neural network quantization as a discrete labelling problem and design an efficient approximate algorithm based on the popular mean-field method. To this end, we devise a projected stochastic gradient descent algorithm and show that it is, in fact, equivalent to a proximal version of the mean-field method. Thus, we provide an MRF optimization perspective to neural network quantization, which would enable research on modelling higher-order interactions among the network parameters to design better quantization schemes. Our experiments on standard image classification datasets with convolutional and residual architectures evidence that our algorithm obtains fully-quantized networks with accuracies very close to the floating-point reference networks.
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