Efficient deep ensembles by averaging neural networks in parameter space

Although deep ensembles provide large accuracy boosts relative to individual models, their use is not widespread in environments in which computational constraints are limited, as deep ensembles require storing M models and require M forward passes at prediction time. We propose a novel, computationally efficient alternative, which we name permAVG. Although deep ensembles cannot simply be average in parameter space, as all models find distinct perhaps distant local optima, permAVG exploits the symmetries of the loss landscape by learning permutations, such that all M models can be permuted into the same local optimum and can thereafter safely be averaged

Automatic discovery of discriminative parts as a quadratic assignment problem

Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built. This paper addresses the question of how to automatically learn such parts from a set of labeled training images. The training of parts is cast as a quadratic assignment problem in which optimal correspondences between image regions and parts are automatically learned. The paper analyses different assignment strategies and thoroughly evaluates them on two public datasets: Willow actions and MIT 67 scenes. State-of-the art results are obtained on these datasets

Automatic discovery of discriminative parts as a quadratic assignment problem

International audiencePart-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built. This paper addresses the question of how to automatically learn such parts from a set of labeled training images. We propose to cast the training of parts as a quadratic assignment problem in which optimal correspondences between image regions and parts are automatically learned. The paper analyses different assignment strategies and thoroughly evaluates them on two public datasets: Willow actions and MIT 67 scenes