Lateral Connections in Denoising Autoencoders Support Supervised Learning
This addresses the challenge of enhancing supervised learning performance for tasks like image classification, though it appears incremental as it builds on existing autoencoder methods.
The paper tackled the problem of improving supervised learning by using a deep denoising autoencoder with lateral connections as an auxiliary unsupervised task, resulting in significant state-of-the-art improvements in permutation-invariant MNIST classification.
We show how a deep denoising autoencoder with lateral connections can be used as an auxiliary unsupervised learning task to support supervised learning. The proposed model is trained to minimize simultaneously the sum of supervised and unsupervised cost functions by back-propagation, avoiding the need for layer-wise pretraining. It improves the state of the art significantly in the permutation-invariant MNIST classification task.