Deep Fried Convnets

Abstract

The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the network parameters. Reducing the number of parameters while preserving essentially the same predictive performance is critically important for operating deep neural networks in memory constrained environments such as GPUs or embedded devices. In this paper we show how kernel methods, in particular a single Fastfood layer, can be used to replace all fully connected layers in a deep convolutional neural network. This novel Fastfood layer is also end-to-end trainable in conjunction with convolutional layers, allowing us to combine them into a new architecture, named deep fried convolutional networks, which substantially reduces the memory footprint of convolutional networks trained on MNIST and ImageNet with no drop in predictive performance.

svd experiments included

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Cited by in corpus (13)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
• Learning both Weights and Connections for Efficient Neural Networks
• An exploration of parameter redundancy in deep networks with circulant projections
• Diversity Networks: Neural Network Compression Using Determinantal Point Processes
• Compact Bilinear Pooling
• Quantized Convolutional Neural Networks for Mobile Devices
• Compressing Convolutional Neural Networks
• A Kronecker-factored approximate Fisher matrix for convolution layers
• Deep Kernel Learning
• Training Sparse Neural Networks
• On Binary Embedding using Circulant Matrices
• Recurrent Residual Module for Fast Inference in Videos