Abstract
Deep learning has become one of the most preferred solution for many complex problems. It shows outstanding performance in the field of computer vision to perform tasks like, image classification, object detection, and image generation. Recently, many research efforts are focused on changing the deep learning architecture for widespread application domain. In this paper, we present a comprehensive survey on the various issues and challenges faced by deep learning techniques. Furthermore, we analyze different deep learning architectures to provide the solution for the computer vision tasks along with their importance.
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Acknowledgements
This work is supported by Science and Engineering Research Board (SERB) file number ECR/2017/002419, project entitled as A Robust Medical Image Forensics System for Smart Healthcare, and scheme Early Career Research Award.
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Bagi, R., Dutta, T., Gupta, H.P. (2020). Deep Learning Architectures for Computer Vision Applications: A Study. In: Kolhe, M., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 94. Springer, Singapore. https://doi.org/10.1007/978-981-15-0694-9_56
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DOI: https://doi.org/10.1007/978-981-15-0694-9_56
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