This project implements a convolutional autoencoder for image denoising using the MNIST ... Conv2DTranspose layer (32 filters, 3×3 kernel, stride=2, ReLU activation, same padding) Final Conv2D layer ...

Built a convolutional autoencoder to clean noise from MNIST digits (1, 2, 4, 5, 9). Experimented with different bottleneck sizes: 64, 32, 16, and 8. Compared reconstruction quality visually and ...

The selected significant features were used to train a novel deep-learning classifiers. We designed a graph-informed convolutional autoencoder called GICA to extract high-level features from the ...

This paper proposes CAE-AD, a novel convolutional autoencoder anomaly detection method that relies only on normal operation data for training the intelligent classi-fier. The method also accommodates ...

Convolutional neural networks and autoencoder have a good effect on extracting data features. Combining these two techniques, a predictive model of a combination of convolutional autoencoder(CAE) and ...

Additionally, it considers methods based on artificial intelligence, such as convolutional networks, which are used to identify objects in images in a manner similar to that of human perception. This ...