119,232 research outputs found
Deep Pyramidal Residual Networks
Deep convolutional neural networks (DCNNs) have shown remarkable performance
in image classification tasks in recent years. Generally, deep neural network
architectures are stacks consisting of a large number of convolutional layers,
and they perform downsampling along the spatial dimension via pooling to reduce
memory usage. Concurrently, the feature map dimension (i.e., the number of
channels) is sharply increased at downsampling locations, which is essential to
ensure effective performance because it increases the diversity of high-level
attributes. This also applies to residual networks and is very closely related
to their performance. In this research, instead of sharply increasing the
feature map dimension at units that perform downsampling, we gradually increase
the feature map dimension at all units to involve as many locations as
possible. This design, which is discussed in depth together with our new
insights, has proven to be an effective means of improving generalization
ability. Furthermore, we propose a novel residual unit capable of further
improving the classification accuracy with our new network architecture.
Experiments on benchmark CIFAR-10, CIFAR-100, and ImageNet datasets have shown
that our network architecture has superior generalization ability compared to
the original residual networks. Code is available at
https://github.com/jhkim89/PyramidNet}Comment: Accepted to CVPR 201
A Counterexample to Cover's 2P Conjecture on Gaussian Feedback Capacity
We provide a counterexample to Cover's conjecture that the feedback capacity
of an additive Gaussian noise channel under power constraint
be no greater than the nonfeedback capacity of the same channel under
power constraint , i.e., .Comment: 2 pages, submitted to IEEE Transactions on Information Theor
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