  A semi-supervised learning framework using the feedforward-designed
convolutional neural networks (FF-CNNs) is proposed for image classification in
this work. One unique property of FF-CNNs is that no backpropagation is used in
model parameters determination. Since unlabeled data may not always enhance
semi-supervised learning, we define an effective quality score and use it to
select a subset of unlabeled data in the training process. We conduct
experiments on the MNIST, SVHN, and CIFAR-10 datasets, and show that the
proposed semi-supervised FF-CNN solution outperforms the CNN trained by
backpropagation (BP-CNN) when the amount of labeled data is reduced.
Furthermore, we develop an ensemble system that combines the output decision
vectors of different semi-supervised FF-CNNs to boost classification accuracy.
The ensemble systems can achieve further performance gains on all three
benchmarking datasets.
