15,954 research outputs found
Going Deeper with Convolutions
We propose a deep convolutional neural network architecture codenamed
"Inception", which was responsible for setting the new state of the art for
classification and detection in the ImageNet Large-Scale Visual Recognition
Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the
improved utilization of the computing resources inside the network. This was
achieved by a carefully crafted design that allows for increasing the depth and
width of the network while keeping the computational budget constant. To
optimize quality, the architectural decisions were based on the Hebbian
principle and the intuition of multi-scale processing. One particular
incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22
layers deep network, the quality of which is assessed in the context of
classification and detection
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
Analyzing Digital Image by Deep Learning for Melanoma Diagnosis
Image classi cation is an important task in many medical
applications, in order to achieve an adequate diagnostic of di erent le-
sions. Melanoma is a frequent kind of skin cancer, which most of them
can be detected by visual exploration. Heterogeneity and database size
are the most important di culties to overcome in order to obtain a good
classi cation performance. In this work, a deep learning based method
for accurate classi cation of wound regions is proposed. Raw images are
fed into a Convolutional Neural Network (CNN) producing a probability
of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were
used due to their well-known e ectiveness. Moreover, data augmentation
was used to increase the number of input images. Experiments show that
the compared models can achieve high performance in terms of mean ac-
curacy with very few data and without any preprocessing.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
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