In autoencoders, the image must be unrolled into a single vector and the network must be built following the constraint on the number of inputs. The block diagram of a Convolutional Autoencoder is ...

Convolutional ... enhancing the autoencoder's ability to discern signal from noise. Denoising Autoencoders learn to recover clean data from inputs corrupted by noise. This architecture is helpful ...

In this project, we employ a convolutional autoencoder architecture for image reconstruction tasks. The model starts with a convolutional layer with 64 filters and a kernel size of (3,3), followed by ...

This paper proposes and implements a deep convolutional autoencoder architecture that maximizes the image colorization performance on two different datasets, the Fruit-360 and Flickr-Faces-HQ. To this ...

In addition, a deep convolutional autoencoder architecture for clustering data was implemented. The paper describes in detail the processing and preparation of the audio files, which included the ...

The architecture of the model is shown in Figure 5. Figure 5. Model architecture diagram of the deep convolutional autoencoder. The input to the model is a 9 × 24 matrix, where 9 represents the 9 ...