… of convolution neural network … convolution autoencoder to the simple autoencoder in
different tasks: image compression and image de-noising. We show that convolution autoencoder …

… Therefore, this paper proposes a convolutional autoencoder … convolutional autoencoder
approach can be extended for … architecture based on convolutional autoencoder neural network (…

… we proposed a convolutional autoencoder based image … neural networks in [16], into the
loss function to improve the MS-SSIM performance. Besides, the generative adversarial network …

… Deep clustering utilizes deep neural networks to learn feature representation that is suitable
… not well take advantage of convolutional neural networks or do not considerably preserve …

… convolutional sparse auto-encoder (CSAE), which leverages the structure of the convolutional
… We employed the features learned in the CSAE to initialize convolutional neural networks …

… This paper proposes a novel hybrid model based on Convolutional Autoencoder (CAE)
network and Convolutional Neural Network (CNN) for automatic plant disease detection. To the …

… by combining a convolutional autoencoder with convolutional neural network, which is
called CAE-CNN (Convolutional AutoEncoder and Convolutional Neural Network). Specifically, …

… of convolutional neural network … convolutional auto-encoder network (CAE) to support CNN
modeling for synthesis of multi-geoinformation. First, two simple unsupervised CAE networks …

… In the next section we present our convolutional autoencoder based approach, which does
not incorporate any domain-specific knowledge, and in fact is operating solely on the raw …

… Because of the powerful feature learning ability, deep convolution neural network (CNN) has
… [23] proposed a convolutional auto-encoder, which is an unsupervised learning method for …