The DOI matches its Crossref record; 41 of the 45 references it cites were found in a registry or an index.
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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Items 201–215 of 215
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Mamiya, A., & Yamasaki, N. (2022) · International Journal of Networking and Computing · doi:10.15803/ijnc.12.2_295
Efficient Algorithms for Artificial Neural Networks and Explainable AI
Eshkiki, H. (2023) · Cronfa (Swansea University) · doi:10.23889/suthesis.63943
Asriani, E., Muchtadi-Alamsyah, I., & Purwarianti, A. (2024) · Mathematics · doi:10.3390/math12111697
Kissel, M., & Diepold, K. J. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74640-6_9
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Chen, J., & Chen, Z. (2025) · Applied Sciences · doi:10.3390/app15179356
Hamidi, M., Azhdari, P., & Vajargah, K. F. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-02928-9
Aging-Aware Character Recognition with E-Textile Inputs
Lin, J., Rong, Y., Cheng, Y., & He, C. Y. (2025) · Electronics · doi:10.3390/electronics14193964
Human Action Recognition in Videos with Shallow and Deep Learning
Phan, T. H. H. (2019) · theses.fr (ABES) · doi:10.70675/fa813329z9321z4fc2za918zd4282f6ec3ac
Towards an understanding of neural networks : mean-field incursions
Gabrié, M. (2019) · theses.fr (ABES) · doi:10.70675/23ab2e47z0b44z4a96za279z784e7b7cfd07 · Cited by 4
Efficient and privacy-preserving compressive learning
Chatalic, A. (2020) · theses.fr (ABES) · doi:10.70675/32098958z1deez4f21zb51az429b616621fd · Cited by 2
Approximations parcimonieuses et méthodes à noyaux pour la compression de modèles d'apprentissage
Giffon, L. (2020) · theses.fr (ABES) · doi:10.70675/7c477a6fz884bz4705z871fz046d746e8042
Improving Scalability and Inference in Probabilistic Deep Models
Rossi, S. (2022) · theses.fr (ABES) · doi:10.70675/807ea578zd70dz4898zb642zb1cdc00dcd86
Random features for dot product kernels and beyond
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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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