Design of ADNN, an adaptive deep neural network which results in fast and energy- efficient decision making (inference). Joint optimization of all the exit points in ADNN such that the overall loss is minimized. Results: Experiments on MNIST dataset show that 41.9% of samples exit at the ...
In this paper, we present the depth-adaptive deep neural network using a depth map for semantic segmentation. Typical deep neural networks receive inputs at the predetermined locations regardless of the distance from the camera. This fixed receptive field presents a challenge to generalize the featur...
Star Topology FCN 如上图,经过FN层之后,表征z^{'}会进入到一种星形拓扑结构的多层全连接神经网络,称为star topology multi-layer fully-connected neural network (star topology FCN)。 star topology FCN包括共享的中心FCN和多个独立的场景FCN,因此总的FCN数量为M+1。第p个场景的输出是由共享的中心FCN和场景特...
Kung. Branchynet: Fast inference via early exiting from deep neural networks. ICPR, 2016.],而ACT和SACT的设计更为优雅,理论建模质量更高,所以可以算一种非常经典的破坏式自适应结构了。 ———分割线:下面是一些胡乱的联想,不用当真——— 除此之外,ACT和SACT也展现出了很多同时期或者后期研究工作的雏形...
Fig. 1.(A) A shallow (one hidden layer) and (B) a deep (multiple hidden layers) neural network. Various nonlinear functions have been proposed for approximation, pattern recognition, and classification problems. In MLP, the nodes in successive layers are connected and the connections are weight...
Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method ...
The dynamic balance between the excitatory and inhibitory neurons accelerates the convergence of the neural networks and improves their performance. We use the combination of the two mechanisms to propose a deep SNN with adaptive self-feedback and balanced excitatory–inhibitory neurons (BackEISNN). ...
Accurate deep neural network inference using computational phase-change memory. Nat. Commun. 11, 1–13 (2020). Article ADS CAS Google Scholar Download references Acknowledgements Authors would like to thank Prof. Tuo-Hung Hou from NCTU, Taiwan for providing HfO2/TiO2 RRAM devices. PI: M.S. ...
[21]Nian Cai, Zhenghang Su, Zhineng Lin, Han Wang, Zhijing Yang, and Bingo Wing-Kuen Ling. Blind inpainting using the fully convolutional neural network. The Visual Computer, 2017. [22]Yang Liu, Jinshan Pan, and Zhixun Su. Deep blind image inpainting. In IScIDE, 2019. ...
首先是一个DeepNetwork,起作用是在一视频帧上提取视频特征。主体由一个经典VGG-16网络构成,对任意的一帧x_{t},对应提取的特征为\phi\left( x_{t} \right)\in R^{4096} 在获得了帧级别的特征\phi\left( x_{1} \right),...,\phi\left( x_{T} \right)后,如上图所示,将其送到一个名为Adaptive...