Application of Reinforcement Learning to Stacked Autoencoder Deep Network Architecture Optimization

2018 ◽  
pp. 267-276
Author(s):  
Roman Zajdel ◽  
Maciej Kusy
Keyword(s):  
Reinforcement Learning ◽  
Network Architecture ◽  
Deep Network ◽  
Stacked Autoencoder ◽  
Architecture Optimization

scholarly journals An aggregate method for thorax diseases classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bayu Adhi Nugroho
Keyword(s):  
Network Architecture ◽  
Classification Problem ◽  
Calculation Algorithm ◽  
Training Pattern ◽  
Deep Network ◽  
Network Training ◽  
Chest X Ray ◽  
Medical Image Classification ◽  
Positive Pattern

AbstractA common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method.

scholarly journals The network architecture optimization based on the server load balancing technology

2021 ◽  
Vol 336 ◽  
pp. 08001
Author(s):  
Ruicai Huo ◽  
Songqiu Liu ◽  
Shiwei He
Keyword(s):  
Load Balancing ◽  
Resource Utilization ◽  
Network Architecture ◽  
Integration Method ◽  
Single Point ◽  
Gateway System ◽  
Architecture Optimization ◽  
Cluster Design

The paper takes the authentication gateway system for internal office network as the research object, for the performance bottleneck and single point of failure problem of the single authentication gateway deployed in the existing primary and standby modes, chooses an authentication gateway integration method based on CA certificate. The practical results show that: through implementing the authentication gateway cluster design and introducing load balancing mechanism, this method solves the performance, stability and single point of failure of the authentication gateway, and improves the resource utilization of the authentication gateway device. This method introduced in this paper can be used for reference for the network architecture optimization based on the server load balancing technology.

scholarly journals T–S Fuzzy Model Based Multi-Branch Deep Network Architecture

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 155039-155046
Author(s):  
Faguang Wang ◽  
Yue Wang ◽  
Hongmei Wang ◽  
Chaogang Tang
Keyword(s):  
Network Architecture ◽  
Fuzzy Model ◽  
Deep Network ◽  
Model Based

scholarly journals Understanding unconventional preprocessors in deep convolutional neural networks for face identification

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Chollette C. Olisah ◽  
Lyndon Smith
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Color Space ◽  
Back Propagation ◽  
Face Identification ◽  
Deep Convolutional Neural Networks ◽  
Softmax Classifier ◽  
Deep Network

Abstract Deep convolutional neural networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, activation function, pooling, batch normalization, forward and back propagation and many more. However, very little emphasis is made on the preprocessor’s module of the network. Therefore, in this paper, the network’s preprocessing module is varied across different preprocessing approaches while keeping constant other facets of the deep network architecture, to investigate the contribution preprocessing makes to the network. Commonly used preprocessors are the data augmentation and normalization and are termed conventional preprocessors. Others are termed the unconventional preprocessors, they are: color space converters; grey-level resolution preprocessors; full-based and plane-based image quantization, Gaussian blur, illumination normalization and insensitive feature preprocessors. To achieve fixed network parameters, CNNs with transfer learning is employed. The aim is to transfer knowledge from the high-level feature vectors of the Inception-V3 network to offline preprocessed LFW target data; and features is trained using the SoftMax classifier for face identification. The experiments show that the discriminative capability of the deep networks can be improved by preprocessing RGB data with some of the unconventional preprocessors before feeding it to the CNNs. However, for best performance, the right setup of preprocessed data with augmentation and/or normalization is required. Summarily, preprocessing data before it is fed to the deep network is found to increase the homogeneity of neighborhood pixels even at reduced bit depth which serves for better storage efficiency.

A novel deep network architecture for reconstructing RGB facial images from thermal for face recognition

2019 ◽  
Vol 78 (18) ◽  
pp. 25259-25271 ◽  
Author(s):  
Andre Litvin ◽  
Kamal Nasrollahi ◽  
Sergio Escalera ◽  
Cagri Ozcinar ◽  
Thomas B. Moeslund ◽  
...  
Keyword(s):  
Face Recognition ◽  
Network Architecture ◽  
Deep Network ◽  
Facial Images

PanNet: A Deep Network Architecture for Pan-Sharpening

2017 ◽  
Author(s):  
Junfeng Yang ◽  
Xueyang Fu ◽  
Yuwen Hu ◽  
Yue Huang ◽  
Xinghao Ding ◽  
...  
Keyword(s):  
Network Architecture ◽  
Deep Network

scholarly journals A Minimum Perturbation Theory of Deep Perceptual Learning

2021 ◽  
Author(s):  
Haozhe Shan ◽  
Haim Sompolinsky
Keyword(s):  
Perceptual Learning ◽  
Network Architecture ◽  
Brain Plasticity ◽  
Deep Structure ◽  
Causal Relation ◽  
Neuronal Response ◽  
Single Layer ◽  
Behavioral Changes ◽  
Large Space ◽  
Deep Network

AbstractPerceptual learning (PL) involves long-lasting improvement in perceptual tasks following extensive training. Such improvement has been found to correlate with modifications in neuronal response properties in early as well as late sensory cortical areas. A major challenge is to dissect the causal relation between modification of the neural circuits and the behavioral changes. Previous theoretical and computational studies of PL have largely focused on single-layer model networks, and thus did not address salient characteristics of PL arising from the multiple-staged “deep” structure of the perceptual system. Here we develop a theory of PL in a deep neuronal network architecture, addressing the questions of how changes induced by PL are distributed across the multiple stages of cortex, and how do the respective changes determine the performance in fine discrimination tasks. We prove that in such tasks, modifications of synaptic weights of early sensory areas are both sufficient and necessary for PL. In addition, optimal synaptic weights in the deep network are not unique but span a large space of solutions. We postulate that, in the brain, plasticity throughout the deep network is distributed such that the resultant perturbation on prior circuit structures is minimized. In contrast to most previous models of PL, the minimum perturbation (MP) learning does not change the network readout weights. Our results provide mechanistic and normative explanations for several important physiological features of PL and reconcile apparently contradictory psychophysical findings.

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