Multifractal Anomaly Detection in Images Via Space-Scale Surrogates

To reduce the computational and memory cost of ConvNets, structured pruning consists in removing features in convolutional and/or fully-connected layers of a pretrained network. As a novel contribution, we study the selection of features as a convex quadratic program with linear constraints. in addition, the updating of weights is treated as the resolution of a linear least-squares system. The quadratic objective function arises from the least-squares reconstruction error on the outputs of pruned layers. Experiments show that our method gives accuracies on par with other reconstruction error-based methods, while achieving a clear gain on computation time.