35,021 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
Combining Residual Networks with LSTMs for Lipreading
We propose an end-to-end deep learning architecture for word-level visual
speech recognition. The system is a combination of spatiotemporal
convolutional, residual and bidirectional Long Short-Term Memory networks. We
train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging
database of 500-size target-words consisting of 1.28sec video excerpts from BBC
TV broadcasts. The proposed network attains word accuracy equal to 83.0,
yielding 6.8 absolute improvement over the current state-of-the-art, without
using information about word boundaries during training or testing.Comment: Submitted to Interspeech 201
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