18,923 research outputs found
Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks
Deep learning is known to be data-hungry, which hinders its application in
many areas of science when datasets are small. Here, we propose to use transfer
learning methods to migrate knowledge between different physical scenarios and
significantly improve the prediction accuracy of artificial neural networks
trained on a small dataset. This method can help reduce the demand for
expensive data by making use of additional inexpensive data. First, we
demonstrate that in predicting the transmission from multilayer photonic film,
the relative error rate is reduced by 46.8% (26.5%) when the source data comes
from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer)
films. Second, we show that the relative error rate is decreased by 22% when
knowledge is transferred between two very different physical scenarios:
transmission from multilayer films and scattering from multilayer
nanoparticles. Finally, we propose a multi-task learning method to improve the
performance of different physical scenarios simultaneously in which each task
only has a small dataset
Xception: Deep Learning with Depthwise Separable Convolutions
We present an interpretation of Inception modules in convolutional neural
networks as being an intermediate step in-between regular convolution and the
depthwise separable convolution operation (a depthwise convolution followed by
a pointwise convolution). In this light, a depthwise separable convolution can
be understood as an Inception module with a maximally large number of towers.
This observation leads us to propose a novel deep convolutional neural network
architecture inspired by Inception, where Inception modules have been replaced
with depthwise separable convolutions. We show that this architecture, dubbed
Xception, slightly outperforms Inception V3 on the ImageNet dataset (which
Inception V3 was designed for), and significantly outperforms Inception V3 on a
larger image classification dataset comprising 350 million images and 17,000
classes. Since the Xception architecture has the same number of parameters as
Inception V3, the performance gains are not due to increased capacity but
rather to a more efficient use of model parameters
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