Deep Trans-layer Unsupervised Networks for Representation Learning

TL;DR

This paper introduces a deep trans-layer unsupervised learning framework that combines simple unsupervised methods with trans-layer feature transfer and block histograms, achieving high accuracy on various recognition tasks with fewer parameters.

Contribution

It proposes a novel deep trans-layer unsupervised network architecture that effectively captures comprehensive features using simple methods like PCA and auto-encoders, with improved performance and reduced complexity.

Findings

Achieved 99.45% accuracy on MNIST.

Attained 75.98% accuracy on Caltech 101.

Reached 87.10% accuracy on LFW.

Abstract

Learning features from massive unlabelled data is a vast prevalent topic for high-level tasks in many machine learning applications. The recent great improvements on benchmark data sets achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters usually requires many tedious tricks and much expertise to tune. However, filters learned by these complex architectures are quite similar to standard hand-crafted features visually. In this paper, unsupervised learning methods, such as PCA or auto-encoder, are employed as the building block to learn filter banks at each layer. The lower layer responses are transferred to the last layer (trans-layer) to form a more complete representation retaining more information. In addition, some beneficial methods such as local contrast normalization and whitening are added to the proposed deep trans-layer networks to further boost performance. The trans-layer representations are followed by block histograms with binary encoder schema to learn translation and rotation invariant representations, which are utilized to do high-level tasks such as recognition and classification. Compared to traditional deep learning methods, the implemented feature learning method has much less parameters and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101 dataset and face verification on LFW dataset. The deep trans-layer unsupervised learning achieves 99.45% accuracy on MNIST dataset, 67.11% accuracy on 15 samples per class and 75.98% accuracy on 30 samples per class on Caltech 101 dataset, 87.10% on LFW dataset.