2 research outputs found
Learning Structure and Strength of CNN Filters for Small Sample Size Training
Convolutional Neural Networks have provided state-of-the-art results in
several computer vision problems. However, due to a large number of parameters
in CNNs, they require a large number of training samples which is a limiting
factor for small sample size problems. To address this limitation, we propose
SSF-CNN which focuses on learning the structure and strength of filters. The
structure of the filter is initialized using a dictionary-based filter learning
algorithm and the strength of the filter is learned using the small sample
training data. The architecture provides the flexibility of training with both
small and large training databases and yields good accuracies even with small
size training data. The effectiveness of the algorithm is first demonstrated on
MNIST, CIFAR10, and NORB databases, with a varying number of training samples.
The results show that SSF-CNN significantly reduces the number of parameters
required for training while providing high accuracies the test databases. On
small sample size problems such as newborn face recognition and Omniglot, it
yields state-of-the-art results. Specifically, on the IIITD Newborn Face
Database, the results demonstrate improvement in rank-1 identification accuracy
by at least 10%.Comment: 10 pages, 9 figures, Accepted in CVPR 201
Unravelling Small Sample Size Problems in the Deep Learning World
The growth and success of deep learning approaches can be attributed to two
major factors: availability of hardware resources and availability of large
number of training samples. For problems with large training databases, deep
learning models have achieved superlative performances. However, there are a
lot of \textit{small sample size or } problems for which it is not
feasible to collect large training databases. It has been observed that deep
learning models do not generalize well on problems and specialized
solutions are required. In this paper, we first present a review of deep
learning algorithms for small sample size problems in which the algorithms are
segregated according to the space in which they operate, i.e. input space,
model space, and feature space. Secondly, we present Dynamic Attention Pooling
approach which focuses on extracting global information from the most
discriminative sub-part of the feature map. The performance of the proposed
dynamic attention pooling is analyzed with state-of-the-art ResNet model on
relatively small publicly available datasets such as SVHN, C10, C100, and
TinyImageNet.Comment: 3 figures, 2 tables, accepted in BigMM 202