6,300 research outputs found
Adaptive optical networks using photorefractive crystals
The capabilities of photorefractive crystals as media for holographic interconnections in neural networks are examined. Limitations on the density of interconnections and the number of holographic associations which can be stored in photorefractive crystals are derived. Optical architectures for implementing various neural schemes are described. Experimental results are presented for one of these architectures
GM-Net: Learning Features with More Efficiency
Deep Convolutional Neural Networks (CNNs) are capable of learning
unprecedentedly effective features from images. Some researchers have struggled
to enhance the parameters' efficiency using grouped convolution. However, the
relation between the optimal number of convolutional groups and the recognition
performance remains an open problem. In this paper, we propose a series of
Basic Units (BUs) and a two-level merging strategy to construct deep CNNs,
referred to as a joint Grouped Merging Net (GM-Net), which can produce joint
grouped and reused deep features while maintaining the feature discriminability
for classification tasks. Our GM-Net architectures with the proposed BU_A
(dense connection) and BU_B (straight mapping) lead to significant reduction in
the number of network parameters and obtain performance improvement in image
classification tasks. Extensive experiments are conducted to validate the
superior performance of the GM-Net than the state-of-the-arts on the benchmark
datasets, e.g., MNIST, CIFAR-10, CIFAR-100 and SVHN.Comment: 6 Pages, 5 figure
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