A Convolutional Autoencoder is a type of neural network designed to learn efficient representations of data, particularly images. It consists of two main parts: an encoder that compresses the input into a lower-dimensional space, and a decoder that reconstructs the original input from this compressed representation. The encoder uses convolutional layers to capture spatial hierarchies in the data, making it effective for image processing tasks. These autoencoders are trained using unsupervised learning, meaning they do not require labeled data. They minimize the difference between the original input and the reconstructed output, allowing the model to learn important features and patterns in the data. This makes them useful for applications like image denoising and anomaly detection.