Autoencoders as dimensionality reduction tools..? I'm trying to get a basic understanding of AutoEncoders. Basically they are neural networks with a very low representation of the original input in some hidden layer, and then a final layer which has exact same dimension as the input but 'reconstructed' from the latent lower-dimensional representation. What I don't understand is what should we do with autoencoders, apart from denoising or compression. For example, would it be possible to use the latent representation as a dimensionality-reducted version of the original input, and then using it to train some Machine Learning Model?
Many thanks,
James

Yes, using some form of autoencoder training/pre-training to create good features has been a successful approach in many areas. E.g. for tabular data, using a denoising autoencoder was the winning approach in a recent Kaggle competition. Autoencoder pre-training (recovering masked features) has been used in the TabNet paper that gained a decent amount of attention. These first two examples were for tabular data, but similar things have long been done (and are still popular) for vision applications as discussed here. However, as you may notice it looks like a lot of the most successful version of autoencoders for pretraining representations have been ones that were trained to re-construct corrupted inputs into the uncorrupted inputs.
Other ideas for what you can use autoencoders for would be file compression (e.g. for video calls, what's minimal information that still produces a video that looks decent to humans, but does not take a lot of bandwidth to transmit?).
