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Status: Unchecked ×Keyword: product quantization ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Compressing Deep Convolutional Networks using Vector Quantization

   Gong, Liu, Yang and Bourdev · arXiv (Cornell University) · 2014

   The authors tested vector quantization methods for shrinking the stored parameters of deep convolutional networks, and report they beat matrix factorization on the dense layers while keeping accuracy close.

   Unchecked2 claims
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   1. UncheckedVector quantization compressed a state-of-the-art image-recognition network 16-24 times on ImageNet's 1000-category task, with about 1% lower accuracy.“For the 1000-category classification task in the ImageNet challenge, we are able to achieve 16-24 times compression of the network with only 1% loss of classification accuracy using the state-of-the-art CNN.”
   2. UncheckedApplying k-means clustering or product quantization to a CNN's weights can give a very good balance between model size and recognition accuracy.“Simply applying k-means clustering to the weights or conducting product quantization can lead to a very good balance between model size and recognition accuracy.”