Training Lightweight Deep Convolutional Neural Networks Using Bag-of-Features Pooling

Convolutional neural networks (CNNs) are predominantly used for several challenging computer vision tasks achieving state-of-the-art performance. However, CNNs are complex models that require the use of powerful hardware, both for training and deploying them. To this end, a quantization-based poolin...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 30; číslo 6; s. 1705 - 1715
Hlavní autori: Passalis, Nikolaos, Tefas, Anastasios
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Backpropagation algorithms
Bag-of-features (BoF)
Basis functions
Computational modeling
Computer architecture
Computer vision
convolutional neural networks (CNNs)
Feature extraction
Image classification
Learning
Lightweight
lightweight neural networks
Measurement
Neural networks
pooling operators
Quantization (signal)
Radial basis function
Task analysis
Task complexity
Training
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:Convolutional neural networks (CNNs) are predominantly used for several challenging computer vision tasks achieving state-of-the-art performance. However, CNNs are complex models that require the use of powerful hardware, both for training and deploying them. To this end, a quantization-based pooling method is proposed in this paper. The proposed method is inspired from the bag-of-features model and can be used for learning more lightweight deep neural networks. Trainable radial basis function neurons are used to quantize the activations of the final convolutional layer, reducing the number of parameters in the network and allowing for natively classifying images of various sizes. The proposed method employs differentiable quantization and aggregation layers leading to an end-to-end trainable CNN architecture. Furthermore, a fast linear variant of the proposed method is introduced and discussed, providing new insight for understanding convolutional neural architectures. The ability of the proposed method to reduce the size of CNNs and increase the performance over other competitive methods is demonstrated using seven data sets and three different learning tasks (classification, regression, and retrieval).
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2018.2872995