A Survey on Deep Learning Methods for Robot Vision <s>  <s> The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification. <s> BIB001 </s> A Survey on Deep Learning Methods for Robot Vision <s>  <s> In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent developments in deep learning. The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of non-linear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial survey, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the recent deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures – deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) – one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed. <s> BIB002 </s> A Survey on Deep Learning Methods for Robot Vision <s>  <s> In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Due to the lack of training data and computing power in early days, it is hard to train a large high-capacity convolutional neural network without overfitting. After the rapid growth in the amount of the annotated data and the recent improvements in the strengths of graphics processor units (GPUs), the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. Besides, we also introduce some applications of convolutional neural networks in computer vision. <s> BIB003
flagship conferences in this area (e.g. CVPR, ECCV, ICCV) use deep learning methods or hybrid approaches that incorporate deep learning. Deep learning has already attracted the attention of the robot vision community [56]  . However, given that new methods and algorithms are usually developed within the computer vision community and then transferred to the robot vision community, the question is whether or not new deep learning solutions to computer vision and recognition problems can be directly transferred to robot vision applications. We believe that this transfer is not straightforward considering the multiple requirements of current deep learning solutions in terms of memory and computational resources, which in many cases include the use of GPUs. Furthermore, following  , we believe that this transfer must consider that robot vision applications have different requirements from standard computer vision applications, such as real-time operation with limited on-board computational resources, and the constraining observational conditions derived from the robot geometry, limited camera resolution, and sensor/object relative pose. Currently, there are several reviews and surveys related to deep learning  [10] BIB003 [12] BIB002 [14]  . However, they are not focused on robotic vision applications, and they do not consider their specific requirements. In this context, the main motivation of our survey is to address the use of deep learning in robot vision. In order to achieve this, first, an overview of deep learning and its use in computer vision is given, which includes a description of the most frequently used neural models and their main application areas. Then, the standard methodology and tools used for designing deep learning-based vision systems are presented. Afterwards, a review of the main current work using deep learning in robot vision is presented, as well as current and future trends related to the use of deep learning in robotics. This survey is intended to be a guide for developers of robot vision systems; therefore the focus is on the practical aspects of the use of deep neural networks rather than on theoretical issues. It is also important to note that as Convolutional Neural Networks  BIB001 are the most commonly used deep neural networks in vision applications, we focus our analysis on them. This paper is organized as follows: in Section 2, the use of deep neural networks in computer vision is described. The historical development of the discipline is presented first; then some basic definitions are given; and finally the most important deep neural models are described, as well as their applications in computer vision. In Section 3, the design process of deep-learning based vision applications, including the case of robot vision systems, is discussed. In Section 4, a review of the recent application of deep learning in robot vision systems is presented. In Section 5 current and future challenges of deep learning in robotic applications are discussed. Finally, in Section 6, conclusions related to the use of deep learning in robotics are drawn. The reader interested in having a practical guide for the use/application of deep learning in robot vision, and not in the basic aspects of deep learning, is referred to Sections 3-6.