Although the training accuracy of deep learning using a deep structure is high, the depth of the deep-learning structure is directly proportional to the generalization error. To address this issue, we propose the auto-classifier, a novel classifier that automatically exploits dimensionality reduction. The proposed classifier contains both classifier and generator networks. It is dedicated to generating separable outcomes based on the label and implicitly capturing the latent variable; simultaneously, the generator network must be able to reconstruct the original data based on the given latent variable. We introduce a cooperative learning mechanism with a new loss function that enables the classifier and generator networks to cooperate to achieve the aforementioned objectives. Extensive experiments were conducted using benchmark datasets. The results revealed that the accuracy of the proposed classifier, without any data augmentation, distortion, or pretraining mechanism, was very competitive with the existing state-of-the-art benchmark datasets.

虽然使用深度结构的深度学习的训练精度很高，但深度学习结构的深度与泛化误差成正比。为了解决这个问题，我们提出了自动分类器，这是一种自动利用降维的新型分类器。所提出的分类器包含分类器和生成器网络。它致力于根据标签生成可分离的结果并隐式捕获潜在变量；同时，生成器网络必须能够根据给定的潜在变量重建原始数据。我们引入了一种具有新损失函数的合作学习机制，使分类器和生成器网络能够合作以实现上述目标。使用基准数据集进行了广泛的实验。