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Article summary:
1. This paper introduces a holistic CNN compression framework, termed LRDKT, which works throughout both convolutional and fully-connected layers.
2. The proposed low-rank decomposition (LRD) scheme removes redundancies across both convolutional kernels and fully-connected matrices, with a novel closed-form solver to improve the efficiency of existing iterative optimization solvers.
3. The proposed model has demonstrated superior performance gains over the state-of-the-art methods on MNIST and ImageNet datasets, as well as transfer learning tasks such as domain adaptation and object detection.
Article analysis:
The article is written in an objective manner and provides evidence for its claims through experiments conducted on two popular datasets (MNIST and ImageNet). The authors have also provided source code and compressed models for further verification of their results. Furthermore, the article does not appear to be biased towards any particular viewpoint or opinion, but rather presents the facts objectively. However, there are some points that could be improved upon in terms of trustworthiness and reliability. For example, the authors do not provide any information about potential risks associated with their proposed model or any possible counterarguments that could be raised against it. Additionally, they do not explore other potential applications of their model beyond those mentioned in the paper. Finally, while the authors have provided evidence for their claims through experiments conducted on two popular datasets (MNIST and ImageNet), it would be beneficial if they had also tested their model on other datasets to further validate their results.