6,211 research outputs found
3-Level Residual Capsule Network for Complex Datasets
The Convolutional Neural Network (CNN) have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks. Capsule Networks have addressed the limitations of CNNs and have shown a great improvement by calculating the pose and transformation of the image. Deeper networks are more powerful than shallow networks but at the same time, more difficult to train. Residual Networks ease the training and have shown evidence that they can give good accuracy with considerable depth. Residual Capsule Network [15] has put the Residual Network and Capsule Network together. Though it did well on simple dataset such as MNIST, the architecture can be improved to do better on complex datasets like CIFAR-10. This brings us to the idea of 3-Level Residual Capsule which not only decreases the number of parameters when compared to the seven-ensemble model, but also performs better on complex datasets when compared to Residual Capsule Network
VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection
Although traffic sign detection has been studied for years and great progress
has been made with the rise of deep learning technique, there are still many
problems remaining to be addressed. For complicated real-world traffic scenes,
there are two main challenges. Firstly, traffic signs are usually small size
objects, which makes it more difficult to detect than large ones; Secondly, it
is hard to distinguish false targets which resemble real traffic signs in
complex street scenes without context information. To handle these problems, we
propose a novel end-to-end deep learning method for traffic sign detection in
complex environments. Our contributions are as follows: 1) We propose a
multi-resolution feature fusion network architecture which exploits densely
connected deconvolution layers with skip connections, and can learn more
effective features for the small size object; 2) We frame the traffic sign
detection as a spatial sequence classification and regression task, and propose
a vertical spatial sequence attention (VSSA) module to gain more context
information for better detection performance. To comprehensively evaluate the
proposed method, we do experiments on several traffic sign datasets as well as
the general object detection dataset and the results have shown the
effectiveness of our proposed method
Advancements in Image Classification using Convolutional Neural Network
Convolutional Neural Network (CNN) is the state-of-the-art for image
classification task. Here we have briefly discussed different components of
CNN. In this paper, We have explained different CNN architectures for image
classification. Through this paper, we have shown advancements in CNN from
LeNet-5 to latest SENet model. We have discussed the model description and
training details of each model. We have also drawn a comparison among those
models.Comment: 9 pages, 15 figures, 3 Tables. Submitted to 2018 Fourth International
Conference on Research in Computational Intelligence and Communication
Networks(ICRCICN 2018
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