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Carrying_Out_CNN_Channel_Pruning_in_a_White_Box.pdf
Source: typeset.io
Article summary:
1. This article discusses the process of carrying out CNN channel pruning in a white box.
2. It reviews various methods for model compression and acceleration, such as ResRep, AMC, NetAdapt, MetaPruning, Approximated Oracle Filter Pruning, and more.
3. It also examines techniques such as Visualizing and Understanding Convolutional Networks, Interpretable Convolutional Neural Networks, Object Detectors Emerge in Deep Scene CNNs, Saliency-Adaptive Sparsity Learning for Neural Network Acceleration, Pruning Channels with Attention Statistics for Deep Network Compression, and more.
Article analysis:
The article is generally reliable and trustworthy. The authors provide a comprehensive overview of the process of carrying out CNN channel pruning in a white box. They review various methods for model compression and acceleration that have been developed over the years and discuss their advantages and disadvantages. Additionally, they examine techniques such as Visualizing and Understanding Convolutional Networks, Interpretable Convolutional Neural Networks, Object Detectors Emerge in Deep Scene CNNs, Saliency-Adaptive Sparsity Learning for Neural Network Acceleration, Pruning Channels with Attention Statistics for Deep Network Compression, etc., which are all relevant to the topic at hand.
The article does not appear to be biased or one-sided in its reporting; it presents both sides of the argument equally without favoring one side over the other. Furthermore, it provides evidence to support its claims by citing relevant research papers throughout the text. There are no unsupported claims or missing points of consideration; all relevant information is included in the article.
The only potential issue with this article is that it does not explore any counterarguments or alternative perspectives on the topic at hand; however this is not necessarily an issue since this type of article does not require such exploration. Additionally there is no promotional content or partiality present in the text; all information presented is objective and unbiased. Finally there are no risks noted in this article since it does not discuss any practical applications of its findings; however this is also not an issue since this type of article does not require such discussion either.