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Despite the outstanding performance of convolutional neural networks (CNNs)for many vision tasks, the required computational cost during inference isproblematic when resources are limited. In this context, we proposeConvolutional Neural Mixture Models (CNMMs), a probabilistic model embedding alarge number of CNNs that can be jointly trained and evaluated in an efficientmanner. Within the proposed framework, we present different mechanisms to prunesubsets of CNNs from the mixture, allowing to easily adapt the computationalcost required for inference. Image classification and semantic segmentationexperiments show that our method achieve excellent accuracy-compute trade-offs.Moreover, unlike most of previous approaches, a single CNMM provides a largerange of operating points along this trade-off, without any re-training.

Adaptative Inference Cost With Convolutional Neural Mixture Models