A Survey on the New Generation of Deep Learning in Image Processing <s> B. FUTURE TRENDS OF DEEP LEARNING IN IMAGE PROCESSING <s> Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62 % error) and CIFAR-100, and a 200-layer ResNet on ImageNet. Code is available at: https://github.com/KaimingHe/resnet-1k-layers. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> B. FUTURE TRENDS OF DEEP LEARNING IN IMAGE PROCESSING <s> Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approaches and perform flow detection on real data. However, real data often encounters an issue of imbalanced data distribution which will lead to a gradient dilution issue. When training a neural network, this problem will not only result in a bias toward the majority class but show the inability to learn from the minority classes. In this paper, we propose an end-to-end trainable Tree-Shaped Deep Neural Network (TSDNN) which classifies the data in a layer-wise manner. To better learn from the minority classes, we propose a Quantity Dependent Backpropagation (QDBP) algorithm which incorporates the knowledge of the disparity between classes. We evaluate our method on an imbalanced data set. Experimental result demonstrates that our approach outperforms the state-of-the-art methods and justifies that the proposed method is able to overcome the difficulty of imbalanced learning. We also conduct a partial flow experiment which shows the feasibility of real-time detection and a zero-shot learning experiment which justifies the generalization capability of deep learning in cyber security. <s> BIB002
Although deep learning has achieved excellent performance in some image processing applications, it still needs considerable effort to tackle the openness mentioned above problems. In particular, it would be precious to develop a safe and efficient deep learning-based image processing system. In the process of image processing and analysis, security issues related to deep learning seem to have rarely mentioned. The security problem here refers to the degradation of generalization performance caused by the vulnerability of the model to several malicious attacks BIB002 . Of course, there are many kinds of malicious attacks, such as forging data sets that are not consistent with the image representation, tampering with the statistical characteristics of some hidden layer parameters, abusing the evaluation index of the model, and so on. It is worth pointing out that the security problem of deep learning is not unrelated to the black box characteristics of this model. Once someone attacked the deep learning system, the possibility of this system repair will decrease  . We expect that security issues will become an essential ingredient and future trend for designing a deep learning system in image processing  . For complex image processing tasks, simple models do not seem to be effective, and useful models are often not simple. Effective models here means that the model can obtain relatively excellent generalization performance, and simple models imply that the capacity of the model is relatively low. For example, ResNet can quickly get more than 90% or even higher test accuracy in CIFAR 10/100 classification task but deep stacked auto-encoder network can only hover around 60% BIB001 . It is generally accepted that the ResNet has a more extensive model capacity than the deep stacked auto-encoder network. Here, the concept of model capacity involves the number of parameters of the model, the topological structure of the model, and the complexity of the model. Note that model complexity is not the only factor that can determine the performance difference between the two deep learning models. It should also include parameter optimization learning mode, training skills, and parameter initialization. However, the quantification of model capacity is still a potential research direction in the future. Then the research result will help us to summarize the models related to deep learning in specific image processing tasks. Further, similar to the wavelet base library, the deep learning library can help us to make an appropriate model selection. Since it is usually expensive to obtain sufficient annotated data in several image processing tasks, unsupervised deep learning have been considered as promising research directions at all time. The recent breakthroughs in unsupervised deep learning like the GAN series can provide a gateway to harness the massive amount of unlabelled image datasets. In more image compression processing tasks, there is a higher requirement for the reversibility of the system, which is different from the deep learning model, including GAN. Therefore, the development of the reversible deep learning model is still a potential research direction in the field of unsupervised learning in the future.