Deep Learning in Mining of Visual Content

Abstract

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Review of white box methods for explanations of convolutional neural networks in image classification tasks

“…Considering a standard CNN, the learned filters at the deeper convolution layers behave similar to the high pass, i.e., derivative filters on the top of Gaussian pyramid (some examples are given in Ref. 23). This would imply that the information contained in the feature maps of the last convolution layer correspond to the main object that has been detected by the network from the given input image x.…”

“…(2) and max pooling are the most commonly used while building CNN classifiers. 23 The last layer of the network has the same dimension as the number of classes in the problem, in the example in Fig. 3, it is 10 implying there are 10 categories of objects to recognize: E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 2 ; 1 1 6 ; 3 0 1 ReLUðtÞ ¼ maxð0; tÞ:…”

White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks

“…Considering a standard CNN, the learned filters at the deeper convolution layers behave similar to the high-pass, i.e. derivative filters on the top of Gaussian pyramid (some examples are given in 21 ). This would imply that the information contained in the feature maps of the last convolution layer correspond to the main object that has been detected by the network from the given input image x.…”

“…( 2) and Max Pooling are the most commonly used while building CNN classifiers. 21 The last layer of the network has the same dimension as the number of classes in the problem, in the example in Fig. 2 it is 10 implying there are 10 categories of objects to recognize.…”

Sensitivity and Dimensionality of Atomic Environment Representations used for Machine Learning Interatomic Potentials

“…Machine Learning (ML) as a predictive modelling tool has gained much attention in recent years in fields ranging from biology 1 , and chemistry 2 to materials science 3,4 , building on decades of successful applications in image recognition, natural language processing and artificial intelligence (AI) 5,6 While machine learning has also been increasingly popular in many fields due to its relatively simplicity of application and powerful prediction features, a key driving force of is the increasing availability of information through data repositories, archives and databases of materials and molecule that include billions of structures 3,7 . In recent years, a large number of ML models for materials and molecules have been developed and many novel representation methods have been proposed [8][9][10][11][12][13][14][15] .…”