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Deep Embedded Complementary and Interactive Information for Multi-View Classification
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Multi-view classification optimally integrates various features from different views to improve classification tasks. Though most of the existing works demonstrate promising performance in various computer vision applications, we observe that they can be further improved by sufficiently utilizing complementary view-specific information, deep interactive information between different views, and the strategy of fusing various views. In this work, we propose a novel multi-view learning framework
doi:10.1609/aaai.v34i04.6122
fatcat:3drk4ngdjbdnldaw7n4ikb2xse