Confucius tri-learning: A paradigm of learning from both good examples and bad examples
Peng Ren, Zongjun Han, Zhiqiang Yu, Bin Zhang
Abstract
Confucius remarked, “When three men meet together, one of them who is anxious to learn can always learn something of the other two. He can profit by the good example of the one and avoid the bad example of the other”. 1 In the light of these remarks, we develop a Confucius tri-learning paradigm of learning from both good examples and bad examples. Specifically, we propose to train three models, i.e., two classifiers and one generator, together. On the one hand, each of the two classifiers can learn from “good” examples provided by the other in a recyclable manner. This reduces the amount of incorrect quasi-labels in training cycles, and thus enables a comprehensive use of unlabeled data to effectively train classifiers. On the other hand, each of the two classifiers can learn from “bad” examples given by the generator. By avoiding the bad examples, the negative impact of the incorrect quasi-labels is further neutralized such that refined classification results are obtained. These advantages are profitable for classification task in the condition that extremely limited labeled data samples are available for training, because the “good” examples augment the labeled data samples for training and the “bad” examples lift the classifiers’ discrimination ability against fake targets. The experiments on the MSTAR, OpenSARShip, and FUSAR-Ship datasets demonstrate that our paradigm gives state-of-the-art results. We release our implementation source code at https://gitee.com/han-zongjun/confucius-tri-learning for public evaluations.