DBNet: A New Generalized Structure Efficient for Classification In this paper, we propose a new deep learning structure named deepbroad network (DBNet) that is efficient for classification task. By modifying the decisionmaking mechanism of the deep structure, the proposed method can improve the testing efficiency while maintaining the testing accuracy. Specifically, the modified convolutional neural networks (CNNs) are first pretrained and used to extract highquality features. And then the dimension of extracted features is reduced by linear mapping. Finally, the broad learning system (BLS) uses processed features to make decisions. Compared with the previous deep structure, the efficiency of the proposed model is improved. Compared with the BLS, the DBNet has better performance. The proposed model is evaluated by using the CIFAR10, CIFAR100 and MNIST datasets. And experimental results show that the DBNet is effective and efficient. Code and models are available at https:github.comYHDangDBNet.

Broad Learning System for Class Incremental Learning The largescale image datasets such as ImageNet and openended photo websites are revealing new challenges to image classification that were not apparent in smaller and fixed sets. In particular, how to handle the dynamically growing datasets efficiently, where not only the amount of training data but also the number of classes increases over time, remains an unexplored problem. In this challenging setting, we study how to employ the Broad Learning System (BLS) to deal with the incremental increases of sample classes in datasets. We first determine whether a new batch of samples is from certain seen classes or unseen classes by two methods based on the statistics theory. Then, the incremental training algorithms are developed to classify unlearned data from new classes. We test our class incremental learning algorithms of BLS on some representative datasets such as Cifar100. The experimental results show that our methods can accurately distinguish seen and unseen classes. And the comparison study demonstrates that our method can be extended to 100 classes on Cifar100 with an acceptable loss of accuracy and performs better compared to other similar class incremental algorithms such as iCaRL.