Deep Learning For Computer Vision Tasks: A review
It provides an overview for researchers and practitioners in computer vision, but is incremental as it summarizes existing methods without novel contributions.
This paper reviews widely used deep learning algorithms applied to computer vision tasks, including image classification, object identification, and semantic segmentation, without presenting new experimental results or concrete numbers.
Deep learning has recently become one of the most popular sub-fields of machine learning owing to its distributed data representation with multiple levels of abstraction. A diverse range of deep learning algorithms are being employed to solve conventional artificial intelligence problems. This paper gives an overview of some of the most widely used deep learning algorithms applied in the field of computer vision. It first inspects the various approaches of deep learning algorithms, followed by a description of their applications in image classification, object identification, image extraction and semantic segmentation in the presence of noise. The paper concludes with the discussion of the future scope and challenges for construction and training of deep neural networks.