On the basis of PCA analysis of the autoencoder features, we pruned the filters such that with 85% reduction in feature size, we have only 2-5% reduction in retrieval scores. Then, we studied filter pruning through L2, L1 and correlation. Next, we plan to employ student-teacher networks and explore similarity constraint for model pruning.
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August 2019
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