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We present DeepCABAC, a novel context-adaptive binary arithmetic coder forcompressing deep neural networks. It quantizes each weight parameter byminimizing a weighted rate-distortion function, which implicitly takes theimpact of quantization on to the accuracy of the network into account.Subsequently, it compresses the quantized values into a bitstreamrepresentation with minimal redundancies. We show that DeepCABAC is able toreach very high compression ratios across a wide set of different networkarchitectures and datasets. For instance, we are able to compress by x63.6 theVGG16 ImageNet model with no loss of accuracy, thus being able to represent theentire network with merely 8.7MB.

DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression