Understanding autoencoders
An autoencoder encodes data and compresses it, then decodes data and decompresses it, which doesn’t seem like a very helpful thing to do. However, it’s what happens during the encoding and decoding process that makes autoencoders useful. For example, during this process, the autoencoder can remove noise from a picture, sound, or video, thus cleaning it up. Autoencoders are simpler than GANs and they’re commonly used today for the following important tasks (in order of relevance):
- Data de-noising
- Data dimensionality reduction
- Teaching how more complex techniques work
- Detail context matching (where the autoencoder receives a small high-resolution piece of an image as input and is able to find it in a lower-resolution target image)
- Toy tasks, such as jigsaw puzzle solving
- Simple image generation
The third use means that anyone taking a class on more advanced machine learning techniques will likely encounter...
