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Learning features from massive unlabelled data is a vast prevalent topic forhigh-level tasks in many machine learning applications. The recent greatimprovements on benchmark data sets achieved by increasingly complexunsupervised learning methods and deep learning models with lots of parametersusually requires many tedious tricks and much expertise to tune. However,filters learned by these complex architectures are quite similar to standardhand-crafted features visually. In this paper, unsupervised learning methods,such as PCA or auto-encoder, are employed as the building block to learn filterbanks at each layer. The lower layer responses are transferred to the lastlayer (trans-layer) to form a more complete representation retaining moreinformation. In addition, some beneficial methods such as local contrastnormalization and whitening are added to the proposed deep trans-layer networksto further boost performance. The trans-layer representations are followed byblock histograms with binary encoder schema to learn translation and rotationinvariant representations, which are utilized to do high-level tasks such asrecognition and classification. Compared to traditional deep learning methods,the implemented feature learning method has much less parameters and isvalidated in several typical experiments, such as digit recognition on MNISTand MNIST variations, object recognition on Caltech 101 dataset and faceverification on LFW dataset. The deep trans-layer unsupervised learningachieves 99.45% accuracy on MNIST dataset, 67.11% accuracy on 15 samples perclass and 75.98% accuracy on 30 samples per class on Caltech 101 dataset,87.10% on LFW dataset.

Deep Trans-layer Unsupervised Networks for Representation Learning