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Deep Fried Convnets
Abstract
The fully-connected layers of deep convolutional neural networks typically contain over 90% of the network parameters. Reducing the number of parameters while preserving predictive performance is critically important for training big models in distributed systems and for deployment in embedded devices. In this paper, we introduce a novel Adaptive Fastfood transform to reparameterize the matrix-vector multiplication of fully connected layers. Reparameterizing a fully connected layer with d inputs and n outputs with the Adaptive Fastfood transform reduces the storage and computational costs costs from O(nd) to O(n) and O(n log d) respectively. Using the Adaptive Fastfood transform in convolutional networks results in what we call a deep fried convnet. These convnets are end-to-end trainable, and enable us to attain substantial reductions in the number of parameters without affecting prediction accuracy on the MNIST and ImageNet datasets.
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Rectified Binary Convolutional Networks with Generative Adversarial Learning
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Metadata
- Authors
- Yang, Z., Moczulski, M., Denil, M., De Freitas, N., Song, L., & Wang, Z.
- Affiliations
- Carnegie Mellon University (US)
- University of Oxford (GB)
- Georgia Institute of Technology (US)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 1476-1483
- OpenAlex
- W2152332944
- Cited by
- 215
- References
- 45
- Citations by year
- 2025: 4 · 2024: 6 · 2023: 10 · 2022: 16 · 2021: 19 · 2020: 36 · 2019: 41 · 2018: 36 · 2017: 21 · 2016: 20 · 2015: 6
- FWCI
- 11.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- fully connected layers, parameter reduction, convolutional neural networks, model compression, end-to-end learning
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