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Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressiveperformance on many computer vision related tasks, such as object detection,image recognition, image retrieval, etc. These achievements benefit from theCNNs outstanding capability to learn the input features with deep layers ofneuron structures and iterative training process. However, these learnedfeatures are hard to identify and interpret from a human vision perspective,causing a lack of understanding of the CNNs internal working mechanism. Toimprove the CNN interpretability, the CNN visualization is well utilized as aqualitative analysis method, which translates the internal features intovisually perceptible patterns. And many CNN visualization works have beenproposed in the literature to interpret the CNN in perspectives of networkstructure, operation, and semantic concept. In this paper, we expect to providea comprehensive survey of several representative CNN visualization methods,including Activation Maximization, Network Inversion, Deconvolutional NeuralNetworks (DeconvNet), and Network Dissection based visualization. These methodsare presented in terms of motivations, algorithms, and experiment results.Based on these visualization methods, we also discuss their practicalapplications to demonstrate the significance of the CNN interpretability inareas of network design, optimization, security enhancement, etc.

How convolutional neural network see the world - A survey of convolutional neural network visualization methods