Current Machine Learning cope with different problems, like text processing, speech processing and image classification. But the most fascinating part of Machine Learning is the possibility to use it to do many creative tasks like creating images. Nowadays, Machine Learning can also make pictures like painters. It is all possible by using autoencoders for wise representation learning.
The most commonly known application of autoencoders is generation of pictures like interiors of houses or even faces and videos. Of course, autoencoders are not limited to these simple tasks.
Autoencoder way of Unsupervised Learning
One of the most interesting properties of autoencoders is the ability to learn from unlabeled data. Standard neural network training requires the large amount of labeled data – at most cases, this job requires human work, that is noticeably slow and costs much.
Autoencoders leverage this problem by treating data as its own target. It is all possible, because autoencoders are neural networks consisting of parts that code data and the other parts that decode data. This approach coerce network to learn the optimal coding of data and the proper recognition of correct images and incorrect ones.
The simple example for describing this kind of training is forging notes and catching criminals based on these notes. After some time of repeating of this process, the criminals will improve their own techniques. This analogy simply describes the work of encoder and decoder.
It’s commonly known that in world of Machine Learning, generative models such as autoencoders are one of the best ways to create the true Artificial Intelligence in the future.
Basically, only few years ago, autoencoders were still the simple models without any perspective to be applied in solving real world problems. Recent work on the special kind of autoencoders, like VAE (Variational Autoencoders) shows promising effects, especially on generated pictures due to the improved quality of its generation.