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Convolutional adaptive denoising autoencoders for hierarchical feature extraction |
Qianjun ZHANG, Lei ZHANG( ) |
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China |
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Abstract Convolutional neural networks (CNNs) are typical structures for deep learning and are widely used in image recognition and classification. However, the random initialization strategy tends to become stuck at local plateaus or even diverge, which results in rather unstable and ineffective solutions in real applications. To address this limitation, we propose a hybrid deep learning CNN-AdapDAE model, which applies the features learned by the AdapDAE algorithm to initialize CNN filters and then train the improved CNN for classification tasks. In this model, AdapDAE is proposed as a CNN pre-training procedure, which adaptively obtains the noise level based on the principle of annealing, by starting with a high level of noise and lowering it as the training progresses. Thus, the features learned by AdapDAE include a combination of features at different levels of granularity. Extensive experimental results on STL-10, CIFAR-10, andMNIST datasets demonstrate that the proposed algorithm performs favorably compared to CNN (random filters), CNNAE (pre-training filters by autoencoder), and a few other unsupervised feature learning methods.
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Keywords
convolutional neural networks
annealing
denoising autoencoder
adaptive noise level
pre-training
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Corresponding Author(s):
Lei ZHANG
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Just Accepted Date: 07 December 2016
Online First Date: 06 March 2018
Issue Date: 04 December 2018
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