96 research outputs found
Convolutional Neural Networks combined with Runge-Kutta Methods
A convolutional neural network for image classification can be constructed
mathematically since it can be regarded as a multi-period dynamical system. In
this paper, a novel approach is proposed to construct network models from the
dynamical systems view. Since a pre-activation residual network can be deemed
an approximation of a time-dependent dynamical system using the forward Euler
method, higher order Runge-Kutta methods (RK methods) can be utilized to build
network models in order to achieve higher accuracy. The model constructed in
such a way is referred to as the Runge-Kutta Convolutional Neural Network
(RKNet). RK methods also provide an interpretation of Dense Convolutional
Networks (DenseNets) and Convolutional Neural Networks with Alternately Updated
Clique (CliqueNets) from the dynamical systems view. The proposed methods are
evaluated on benchmark datasets: CIFAR-10/100, SVHN and ImageNet. The
experimental results are consistent with the theoretical properties of RK
methods and support the dynamical systems interpretation. Moreover, the
experimental results show that the RKNets are superior to the state-of-the-art
network models on CIFAR-10 and on par on CIFAR-100, SVHN and ImageNet
Deep Markov Random Field for Image Modeling
Markov Random Fields (MRFs), a formulation widely used in generative image
modeling, have long been plagued by the lack of expressive power. This issue is
primarily due to the fact that conventional MRFs formulations tend to use
simplistic factors to capture local patterns. In this paper, we move beyond
such limitations, and propose a novel MRF model that uses fully-connected
neurons to express the complex interactions among pixels. Through theoretical
analysis, we reveal an inherent connection between this model and recurrent
neural networks, and thereon derive an approximated feed-forward network that
couples multiple RNNs along opposite directions. This formulation combines the
expressive power of deep neural networks and the cyclic dependency structure of
MRF in a unified model, bringing the modeling capability to a new level. The
feed-forward approximation also allows it to be efficiently learned from data.
Experimental results on a variety of low-level vision tasks show notable
improvement over state-of-the-arts.Comment: Accepted at ECCV 201
Intelligent Tennis Robot Based on a Deep Neural Network
In this paper, an improved you only look once (YOLOv3) algorithm is proposed to make the detection effect better and improve the performance of a tennis ball detection robot. The depth-separable convolution network is combined with the original YOLOv3 and the residual block is added to extract the features of the object. The feature map output by the residual block is merged with the target detection layer through the shortcut layer to improve the network structure of YOLOv3. Both the original model and the improved model are trained by the same tennis ball data set. The results show that the recall is improved from 67.70% to 75.41% and the precision is 88.33%, which outperforms the original 77.18%. The recognition speed of the model is increased by half and the weight is reduced by half after training. All these features provide a great convenience for the application of the deep neural network in embedded devices. Our goal is that the robot is capable of picking up more tennis balls as soon as possible. Inspired by the maximum clique problem (MCP), the pointer network (Ptr-Net) and backtracking algorithm (BA) are utilized to make the robot find the place with the highest concentration of tennis balls. According to the training results, when the number of tennis balls is less than 45, the accuracy of determining the concentration of tennis balls can be as high as 80%.</jats:p
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