Pre-Warm: Initializing Convolutional Filters from First-Batch Patch Dictionaries
TL;DR
Pre-Warm, the proposed methodology, is the rule-based use of both dictionaries: the patch count follows the filter count and a foreground density, both dictionaries are built from a single minibatch, and whichever of principal components or k-means better reconstructs those patches is written into the first half of the filter bank, rather than chosen by a validation search.
Abstract
Random initialization of convolutional filters does not use the training images. Previous work has shown that image patches can be copied into the first layer, and that k-means or principal components of patches can serve as filters. This paper compares four initializations of the first layer of a small convolutional network, with every other factor held fixed: He initialization, random mean-centered patches, principal components of those patches, and k-means centroids. Pre-Warm, our proposed methodology, is the rule-based use of both dictionaries: the patch count follows the filter count and a foreground density, both dictionaries are built from a single minibatch, and whichever of principal components or k-means better reconstructs those patches is written into the first half of the filter bank, rather than chosen by a validation search. The remaining filters stay random. On five datasets, principal components improve CIFAR-10 and CIFAR-100 relative to He initialization, and k-means improves SVHN and MNIST, and is the stronger of the two on Fashion-MNIST; copying raw patches does not reproduce those color-set gains. Use principal components on photographic patches and k-means on stroke-like patches; the first-batch reconstruction check recovers that split.
