Titlebook: Deep Learning in Mining of Visual Content; Akka Zemmari,Jenny Benois-Pineau Book 2020 The Author(s), under exclusive license to Springer N

Monolithic

妨礙

Aidan Beggs,Alexandros Kapravelosd dimension which finally allows a classification decision. We are interested in two operations: convolution and pooling and trace analogy with these operations in a classical Image Processing framework.

橡子

https://doi.org/10.1007/978-3-030-22038-9der those designed for particular data: images. First of all we will expose some general principles, then go into detail layer-by-layer and finally briefly overview most popular convolutional neural networks architectures.

convert

上漲

結(jié)合

cogitate

搜集

Gustatory

Dynamic Content Mining,of possible classes. Such networks have no notion of order in time nor in memory. That is they are not suitable for dynamic content mining like speech recognition, video processing, etc. In this chapter we introduce models able to handle temporality of visual content.

勤勞

Case Study for Digital Cultural Content Mining,hitectural styles and specific architectural structures. We are interested in attention mechanisms in Deep CNNs and explain how real visual attention maps built upon human gaze fixations can help in the training of deep neural networks.