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

FUSC

Supervised Learning Problem Formulation,g consists in grouping similar data points in the description space thus inducing a structure on it. Then the data model can be expressed in terms of space partition. Probably, the most popular of such grouping algorithms in visual content mining is the K-means approach introduced by MacQueen as ear

Vasodilation

Optimization Methods,the loss function. Most of them are iterative and operate by decreasing the loss function following a descent direction. These methods solve the problem when the loss function is supposed to be convex. The main idea can be expressed simply as follows: starting from initial arbitrary (or randomly) ch

一條卷發(fā)

Deep in the Wild,d 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.

GLUE

機(jī)密

可忽略

PET-scan

Introducing Domain Knowledge,is particular application of medical imaging domain, Deep NNs have become the mandatory tool. In this chapter we give some highlights on how the usual steps in design of a Deep Neural Network classifier are implemented in the case when domain knowledge has to be considered. But more than that: faith

思考

2191-5768 eep neural networks and application to digital cultural content mining. An additional application field is also discussed, and illustrates how deep learning can be of very high interest to comp978-3-030-34375-0978-3-030-34376-7Series ISSN 2191-5768 Series E-ISSN 2191-5776

GRATE

孤僻