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Convolutional Neural Networks have provided state-of-the-art results inseveral computer vision problems. However, due to a large number of parametersin CNNs, they require a large number of training samples which is a limitingfactor for small sample size problems. To address this limitation, we proposeSSF-CNN which focuses on learning the structure and strength of filters. Thestructure of the filter is initialized using a dictionary-based filter learningalgorithm and the strength of the filter is learned using the small sampletraining data. The architecture provides the flexibility of training with bothsmall and large training databases and yields good accuracies even with smallsize training data. The effectiveness of the algorithm is first demonstrated onMNIST, CIFAR10, and NORB databases, with a varying number of training samples.The results show that SSF-CNN significantly reduces the number of parametersrequired for training while providing high accuracies the test databases. Onsmall sample size problems such as newborn face recognition and Omniglot, ityields state-of-the-art results. Specifically, on the IIITD Newborn FaceDatabase, the results demonstrate improvement in rank-1 identification accuracyby at least 10%.

Learning Structure and Strength of CNN Filters for Small Sample Size Training