Modulated Convolutional Networks

Baochang Zhang, Runqi Wang, Xiaodi Wang, Jungong Han, Rongrong Ji

Research output: Contribution to journalArticlepeer-review

5 Citations (SciVal)
196 Downloads (Pure)

Abstract

While the deep convolutional neural network (DCNN) has achieved overwhelming success in various vision tasks, its heavy computational and storage overhead hinders the practical use of resource-constrained devices. Recently, compressing DCNN models has attracted increasing attention, where binarization-based schemes have generated great research popularity due to their high compression rate. In this article, we propose modulated convolutional networks (MCNs) to obtain binarized DCNNs with high performance. We lead a new architecture in MCNs to efficiently fuse the multiple features and achieve a similar performance as the full-precision model. The calculation of MCNs is theoretically reformulated as a discrete optimization problem to build binarized DCNNs, for the first time, which jointly consider the filter loss, center loss, and softmax loss in a unified framework. Our MCNs are generic and can decompose full-precision filters in DCNNs, e.g., conventional DCNNs, VGG, AlexNet, ResNets, or Wide-ResNets, into a compact set of binarized filters which are optimized based on a projection function and a new updated rule during the backpropagation. Moreover, we propose modulation filters (M-Filters) to recover filters from binarized ones, which lead to a specific architecture to calculate the network model. Our proposed MCNs substantially reduce the storage cost of convolutional filters by a factor of 32 with a comparable performance to the full-precision counterparts, achieving much better performance than other state-of-the-art binarized models.
Original languageEnglish
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Early online date09 Mar 2021
DOIs
Publication statusE-pub ahead of print - 09 Mar 2021

Keywords

  • Binarized filters
  • Computational modeling
  • Computer architecture
  • Convolution
  • deep convolutional neural network (DCNN)
  • discrete optimization
  • modulated convolutional networks (MCNs).
  • Modulation
  • Object detection
  • Quantization (signal)
  • Task analysis

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