Adaptative Inference Cost With Convolutional Neural Mixture Models

ICCV 2019 Adria RuizJakob Verbeek

Despite the outstanding performance of convolutional neural networks (CNNs) for many vision tasks, the required computational cost during inference is problematic when resources are limited. In this context, we propose Convolutional Neural Mixture Models (CNMMs), a probabilistic model embedding a large number of CNNs that can be jointly trained and evaluated in an efficient manner... (read more)

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