人工神经网络(Artificial Neural Network,即ANN),它从信息处理角度对人脑神经元网络进行抽象,建立某种简单模型,按不同的连接方式组成不同的网络。在工程与学术界也常直接简称为神经网络或类神经网络。神经网络是一种运算模型,由大量的节点(或称神经元)之间相互联接构成。每个节点代表一种特定的输出函数,称为激励函数(activation function)。每两个节点间的连接都代表一个对于通过该连接信号的加权值,称之为权重,这相当于人工神经网络的记忆。网络的输出则依网络的连接方式,权重值和激励函数的不同而不同。而网络自身通常都是对自然界某种算法或者函数的逼近,也可能是对一种逻辑策略的表达。

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单幅图像超分辨率重建是计算机视觉领域上的一个重要问题, 在安防视频监控、飞机航拍以及卫星遥感等方面具有重要的研究意义和应用价值. 近年来, 深度学习在图像分类、检测、识别等诸多领域中取得了突破性进展, 也推动着图像超分辨率重建技术的发展. 本文首先介绍单幅图像超分辨率重建的常用公共图像数据集; 然后重点阐述基于深度学习的单幅图像超分辨率重建方向的创新与进展; 最后讨论了单幅图像超分辨率重建方向上存在的困难和挑战, 并对未来的发展趋势进行了思考与展望.

http://www.aas.net.cn/cn/article/doi/10.16383/j.aas.c190859

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Artificial Intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to develop means to endow common people with a minimum understanding of what AI means. Combining visual programming and WiSARD weightless artificial neural networks, this article presents a new methodology, AI from concrete to abstract (AIcon2abs), to enable general people (including children) to achieve this goal. The main strategy adopted by is to promote a demystification of artificial intelligence via practical activities related to the development of learning machines, as well as through the observation of their learning process. Thus, it is possible to provide subjects with skills that contributes to making them insightful actors in debates and decisions involving the adoption of artificial intelligence mechanisms. Currently, existing approaches to the teaching of basic AI concepts through programming treat machine intelligence as an external element/module. After being trained, that external module is coupled to the main application being developed by the learners. In the methodology herein presented, both training and classification tasks are blocks that compose the main program, just as the other programming constructs. As a beneficial side effect of AIcon2abs, the difference between a program capable of learning from data and a conventional computer program becomes more evident. In addition, the simplicity of the WiSARD weightless artificial neural network model enables easy visualization and understanding of training and classification tasks internal realization.

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Artificial Intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to develop means to endow common people with a minimum understanding of what AI means. Combining visual programming and WiSARD weightless artificial neural networks, this article presents a new methodology, AI from concrete to abstract (AIcon2abs), to enable general people (including children) to achieve this goal. The main strategy adopted by is to promote a demystification of artificial intelligence via practical activities related to the development of learning machines, as well as through the observation of their learning process. Thus, it is possible to provide subjects with skills that contributes to making them insightful actors in debates and decisions involving the adoption of artificial intelligence mechanisms. Currently, existing approaches to the teaching of basic AI concepts through programming treat machine intelligence as an external element/module. After being trained, that external module is coupled to the main application being developed by the learners. In the methodology herein presented, both training and classification tasks are blocks that compose the main program, just as the other programming constructs. As a beneficial side effect of AIcon2abs, the difference between a program capable of learning from data and a conventional computer program becomes more evident. In addition, the simplicity of the WiSARD weightless artificial neural network model enables easy visualization and understanding of training and classification tasks internal realization.

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