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In this work, a novel pruning framework is introduced to compress noisy or less discriminant filters in small fractional steps, in deep convolutional networks. The proposed framework utilizes a class-separability criterion that can exploit effectively the labeling information in annotated training sets. Additionally, an asymptotic schedule for the pruning rate and scaling factor is adopted so that the selected filters’ weights collapse gradually to zero, providing improved robustness. Experimental results on the CIFAR-10, Google speech commands (GSC) and ImageNet32 (a downsampled version of ILSVRC-2012) show the efficacy of the proposed approach.
Structured Pruning of LSTMs via Eigenanalysis and Geometric Median. This code can be used for generating more compact LSTMs, which is very useful for mobile multimedia applications and deep learning applications in other resource-constrained environments.
Subclass deep neural networks
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