Image Classification Using Deep Autoencoders
Munmi Gogoi, Shahin Ara Begum · 2017
Deep learning refers to computational models comprising of several processing layers that permit display of data with a compound level of abstraction. Till date, several deep learning architectures have been developed, and notable results are attained. The best result often involves an unsupervised pretraining phase followed by supervised learning task. In this work, a particular implementation of deep autoencoders with SVM (Support Vector Machine) layer as a classification layer on the top of the encoding layer is explored. A comparison is made on MNIST dataset with softmax regression function layer and SVM layer as a classification layer with 2 layers and 3 layers SAE (Stack Autoencoders) respectively. Experimental results are evaluated using SAE. It is observed that SVM as classification layer obtains 99.8% accuracy with 0.2% error rate and outperforms softmax regression layer as a classification layer in autoencoders. Further, affect of varying number of neurons in the hidden layers of the autoencoders on the network performance with regard to classification accuracy is also studied.