Variants exist, aiming to force the learned representations to assume useful properties.[3] Examples are regularized autoencoders (Sparse, Denoising and Contractive), which are effective in learning representations for subsequent classification tasks,[4] and Variational autoencoders, with applications as generative models.[5] Autoencoders are applied to many problems, including facial recognition,[6] feature detection,[7] anomaly detection and acquiring the meaning of words.[8][9] Autoencoders are also generative models which can randomly generate new data that is similar to the input data (training data).[7]
^Hinton GE, Krizhevsky A, Wang SD. Transforming auto-encoders. In International Conference on Artificial Neural Networks 2011 Jun 14 (pp. 44-51). Springer, Berlin, Heidelberg.
^ abGéron, Aurélien (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Canada: O’Reilly Media, Inc. pp. 739–740.