基于协同梯度下降的可信学习方法

Artificial intelligence enables human beings to transform and adapt to nature to a higher stage, which is a revolution in the development of human society. However, the poor interpretability of deep learning is currently the bottleneck for artificial intelligence. The reasoning process based on the deep model is a black box, and the existing theories cannot fully explain the reasons for the output results of the model, and the research on it is still at a relatively early stage. To improve the interpretability of bilinear models, we put forward a dependable learning approach based on the cogradient descent algorithm. A feedback mechanism is introduced in the training process to realize decoupling and causality consolidation, which improves the interpretability and performance of the model. In the tasks of convolutional neural network training and model compression, the results prove the effectiveness and applicability of the method.