Convolutional Autoencoders

Convolutional autoencoders are a type of machine learning model designed to learn efficient representations of images. They consist of two parts: an encoder that compresses the image into a smaller, meaningful summary, and a decoder that reconstructs the original image from this summary. Using convolutional layers, which are good at capturing spatial patterns in images, these autoencoders can remove noise, reduce data size, or learn features for tasks like image recognition. Essentially, they’re tools that understand and recreate images by focusing on important details, enabling applications across image processing and analysis.