A Survey on the New Generation of Deep Learning in Image Processing <s> 3) BLS <s> There are many innovations in imaging sensors designs. A bottleneck in evaluating proposed designs is the need to create optimal processing algorithms. I describe a pipeline architecture and simulation methods that automate the pipeline design. Article not available. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 3) BLS <s> Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as “mapped features” in feature nodes and the structure is expanded in wide sense in the “enhancement nodes.” The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS. <s> BIB002
RVFL can offer a different conventional BP learning method, which can view as the foundation or basic idea of ELM and BLS BIB002 . The BLS can be expanded broadly when new feature nodes and enhancement nodes are needed, see in Fig.17 . Compare to ELM, the main advantage of BLS is that incremental learnings can rapidly update and remodel the system. Moreover, BLS can be applied to an extended network or to a network that only needs to compute the connecting weights of the last layer, such as ELM. In BLS, to take the advantages of sparse auto-encoder characteristics (the same with ELM sparse auto-encoder), we have H 1,k = σ (X β T 1,k ) for the kth window, k = 1, 2, . . . , K . whereX = [X , 1] and β 1,k can be solved by Eq. (47), where R 1,k = σ (X W 1,k + b 1,k ), and W 1,k and b 1,k are randomly choose. In addition, the number of hidden nodes at each window is same. After acquiring H 1,k , k = 1, 2, . . . , K , we can obtain H 1 by cascading operation, that is, H BIB001  And then we can obtain H 2 by the following operation, where W and b are randomly choose weight matrix and bias, and σ is non-linear active function. To maintain the information of H 1 , H can be acquired by cascading H 2 and H 1 . Finally, the parameter β can solve by the following linear system, That is, β = H † Y . Further, for the incremental learning of the dynamic expansion of the BLS model, and simplification BLS model using singular value decomposition, one can refer to relevant literature.