{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tensor-regression-networks-with-various-low","title":"Tensor Regression Networks with various Low-Rank Tensor Approximations","arxiv_id":"1712.09520","date":"2017-12-27","proceeding":null,"authors":["Xingwei Cao","Guillaume Rabusseau"],"abstract":"Tensor regression networks achieve high compression rate of neural networks\nwhile having slight impact on performances. They do so by imposing low tensor\nrank structure on the weight matrices of fully connected layers. In recent\nyears, tensor regression networks have been investigated from the perspective\nof their compressive power, however, the regularization effect of enforcing\nlow-rank tensor structure has not been investigated enough. We study tensor\nregression networks using various low-rank tensor approximations, aiming to\ncompare the compressive and regularization power of different low-rank\nconstraints. We evaluate the compressive and regularization performances of the\nproposed model with both deep and shallow convolutional neural networks. The\noutcome of our experiment suggests the superiority of Global Average Pooling\nLayer over Tensor Regression Layer when applied to deep convolutional neural\nnetwork with CIFAR-10 dataset. On the contrary, shallow convolutional neural\nnetworks with tensor regression layer and dropout achieved lower test error\nthan both Global Average Pooling and fully-connected layer with dropout\nfunction when trained with a small number of samples.","url_abs":"http://arxiv.org/abs/1712.09520v2","url_pdf":"http://arxiv.org/pdf/1712.09520v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tensor-regression-networks-with-various-low","repo_url":"https://github.com/Vixaer/LowRankTRN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"tensor-regression-networks-with-various-low","repo_url":"https://github.com/xwcao/LowRankTRN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}