So far, in the previous chapters, we have learned about classifying images by training a model based on the input image and its corresponding label. Now let's imagine a scenario where we need to cluster images based on their similarity and with the constraint of not having their corresponding labels. Autoencoders come in handy to identify and group similar images.
An autoencoder takes an image as input, stores it in a lower dimension, and tries to reproduce the same image as output, hence the term auto (which stands for being able to reproduce the input). However, if we just reproduce the input in the output, we would not need a network, but a simple multiplication of the input by 1 would do. The differentiating aspect of an autoencoder is that it encodes the information present in an image in a lower dimension and then reproduces the image, hence the term encoder (which stands for representing the information of an image in a lower dimension). This way...
