Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification
This work addresses the challenge of enhancing supervised learning performance in computer vision by integrating unsupervised methods, though it is incremental as it builds on existing architectures.
The authors tackled the problem of improving large-scale image classification by augmenting supervised neural networks with unsupervised reconstruction objectives, resulting in a noticeable improvement in validation-set accuracy over a strong VGGNet baseline on ImageNet.
Unsupervised learning and supervised learning are key research topics in deep learning. However, as high-capacity supervised neural networks trained with a large amount of labels have achieved remarkable success in many computer vision tasks, the availability of large-scale labeled images reduced the significance of unsupervised learning. Inspired by the recent trend toward revisiting the importance of unsupervised learning, we investigate joint supervised and unsupervised learning in a large-scale setting by augmenting existing neural networks with decoding pathways for reconstruction. First, we demonstrate that the intermediate activations of pretrained large-scale classification networks preserve almost all the information of input images except a portion of local spatial details. Then, by end-to-end training of the entire augmented architecture with the reconstructive objective, we show improvement of the network performance for supervised tasks. We evaluate several variants of autoencoders, including the recently proposed "what-where" autoencoder that uses the encoder pooling switches, to study the importance of the architecture design. Taking the 16-layer VGGNet trained under the ImageNet ILSVRC 2012 protocol as a strong baseline for image classification, our methods improve the validation-set accuracy by a noticeable margin.