34,144 research outputs found
Maxmin convolutional neural networks for image classification
Convolutional neural networks (CNN) are widely used in computer vision,
especially in image classification. However, the way in which information and
invariance properties are encoded through in deep CNN architectures is still an
open question. In this paper, we propose to modify the standard convo- lutional
block of CNN in order to transfer more information layer after layer while
keeping some invariance within the net- work. Our main idea is to exploit both
positive and negative high scores obtained in the convolution maps. This behav-
ior is obtained by modifying the traditional activation func- tion step before
pooling. We are doubling the maps with spe- cific activations functions, called
MaxMin strategy, in order to achieve our pipeline. Extensive experiments on two
classical datasets, MNIST and CIFAR-10, show that our deep MaxMin convolutional
net outperforms standard CNN
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition
Convolutional Neural Networks (CNN) have revolutionized perception for color
images, and their application to sonar images has also obtained good results.
But in general CNNs are difficult to train without a large dataset, need manual
tuning of a considerable number of hyperparameters, and require many careful
decisions by a designer. In this work, we evaluate three common decisions that
need to be made by a CNN designer, namely the performance of transfer learning,
the effect of object/image size and the relation between training set size. We
evaluate three CNN models, namely one based on LeNet, and two based on the Fire
module from SqueezeNet. Our findings are: Transfer learning with an SVM works
very well, even when the train and transfer sets have no classes in common, and
high classification performance can be obtained even when the target dataset is
small. The ADAM optimizer combined with Batch Normalization can make a high
accuracy CNN classifier, even with small image sizes (16 pixels). At least 50
samples per class are required to obtain test accuracy, and using
Dropout with a small dataset helps improve performance, but Batch Normalization
is better when a large dataset is available.Comment: Author version; IEEE/MTS Oceans 2017 Aberdee
Deep Dictionary Learning: A PARametric NETwork Approach
Deep dictionary learning seeks multiple dictionaries at different image
scales to capture complementary coherent characteristics. We propose a method
for learning a hierarchy of synthesis dictionaries with an image classification
goal. The dictionaries and classification parameters are trained by a
classification objective, and the sparse features are extracted by reducing a
reconstruction loss in each layer. The reconstruction objectives in some sense
regularize the classification problem and inject source signal information in
the extracted features. The performance of the proposed hierarchical method
increases by adding more layers, which consequently makes this model easier to
tune and adapt. The proposed algorithm furthermore, shows remarkably lower
fooling rate in presence of adversarial perturbation. The validation of the
proposed approach is based on its classification performance using four
benchmark datasets and is compared to a CNN of similar size
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