Train a convolution-based autoencoder on an image dataset. The autoencoder should be trained to remove noise from the images, similar to the process demonstrated in class. Research on Kaggle and ...

The proposed approach involves an autoencoder with a sparse latent layer for learning sparse codes of wavelet features. The estimated sparse codes are used for assigning classes to test samples using ...

Convolutional Autoencoders are a type of neural network architecture that combines convolutional layers for feature extraction with transpose convolutional layers for upsampling, making them ...

In recent years, medical image segmentation based on ... we enhance the source domain with a convolutional autoencoder to improve the generalization ability of the model. Then, we introduce an ...

Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during ... fully convolutional autoencoder. Although it is ...

To address the limitation that the extreme learning machine autoencoder cannot use topological information, the graph convolution operation is introduced between the input layer and the hidden layer ...

In order to overcome the disadvantage that traditional machine learning ... CNN and stacked autoencoder (SAE) for emotion recognition. Compared with other algorithms, the proposed algorithm makes full ...