 A study published in 2015 empirically showed that the joint training method not only learns better data models, but also learned more representative features for classification as compared to the layerwise method.However, their experiments highlighted how the success of joint training for deep autoencoder architectures depends heavily on the regularization strategies adopted in the modern variants of the model.The two main applications of autoencoders since the 80s have been "dimensionality reduction" and "information retrieval," but modern variations of the basic model were proven successful when applied to different domains and tasks.