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Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization
2021
Entropy
Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as co-training, where multiple complementary views are combined using a small amount of labeled data. A simple way to associate views to visual objects is through the application of a degree of rotation or a type
doi:10.3390/e23040423
pmid:33916017
fatcat:ajfzk2s2b5hbpopngxh6igngsi