A novel technique for deep learning of image classifiers is presented. The learned CNN models offer better separation of deep features (also known as embedded vectors) measured by Euclidean proximity and also no deterioration of the classification results by class membership probability. The latter feature can be used for enhancing image classifiers having the classes at the model's exploiting stage different from from classes during the training stage. While the Shannon information of SoftMax probability for target class is extended for mini-batch by the intra-class variance, the trained network itself is extended by the Hadamard layer with the parameters representing the class centers. Contrary to the existing solutions, this extra neural layer enables interfacing of the training algorithm to the standard stochastic gradient optimizers, e.g. AdaM algorithm. Moreover, this approach makes the computed centroids immediately adapting to the updating embedded vectors and finally getting the comparable accuracy in less epochs.
翻译:展示了一种对图像分类员进行深层学习的新技术。 所学的CNN模型提供了更好的分解深层特征( 也称为嵌入矢量 ), 由欧几里德近距离测量, 分类结果不会按阶级成员概率下降。 后一种特征可用于提高在模型的利用阶段班级与培训阶段的班级不同的图像分类员。 虽然Shonnon关于目标班级SoftMax概率的信息因阶级内部差异而扩大, 但经过培训的网络本身却由Hadamard层以代表阶级中心的参数加以扩展。 与现有的解决方案相反, 这种额外的神经层使得培训算法能够与标准的蒸气梯度优化器( 如AdaM算法 ) 相衔接。 此外, 这种方法使得计算出的机器人能够立即适应更新嵌入式矢量的矢量, 并最终在更少的球中取得可比的精确度。