A Universal Compression Algorithm for Deep Neural Networks
In the past decade, deep neural networks (DNNs) have shown state-of-the-art performance on a wide range of complex machine learning tasks. Many of these results have been achieved while growing the size of DNNs, creating a demand for efficient compression and transmission of them. This talk will present DeepCABAC, a universal compression algorithm for DNNs that through its adaptive, context-based rate modeling, allows an optimal quantization and coding of neural network parameters. It compresses state-of-the-art DNNs up to 1.5% of their original size with no accuracy loss and has been selected as basic compression technology for the emerging MPEG-7 part 17 standard on DNN compression.