Abstract:<p>Deep learning has dramatically improved performance in various image analysis applications in the last few years. However, recent deep learning architectures can be very large, with up to hundreds of layers and millions or even billions of model parameters that are impossible to fit into commodity graphics processing units. We propose a novel approach for compressing high-dimensional activation maps, the most memory-consuming part when training modern deep learning architectures. To this end, we also … Show more
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