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Convolutional Neural Network Layers and Architectures
Keywords:
computer vision, deep learning, image recognition, image classification, object recognition.
Abstract:
In recent years, computer vision which is one of the fastest growing artificial intelligence disciplines, has become increasingly important in our society due to its wide range applications in different areas such as health care and medicine (algorithms that can diagnose medical images for diseases), vision- based robotics, self-driving cars (that can see and drive safely). Convolutional neural networks are biologically inspired architectures and represent the core of deep learning algorithms in computer vision. In this paper, we represent the fundamental building blocks of convolutional neural networks and the most popular convolutional neural network architectures in the history, including those that have achieved the state-of-the-art performance on standard recognition datasets and tasks such as ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). ILSVRC is one of the largest challenges in computer vision organized by Stanford Vision Lab since 2010 and every year teams compete to claim the state-of-the-art performance on the dataset.
CITATION:
IEEE format
T. Bezdan, N. Bačanin Džakula, “Convolutional Neural Network Layers and Architectures,” in Sinteza 2019 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2019, pp. 445-451. doi:10.15308/Sinteza-2019-445-451
APA format
Bezdan, T., Bačanin Džakula, N. (2019). Convolutional Neural Network Layers and Architectures. Paper presented at Sinteza 2019 - International Scientific Conference on Information Technology and Data Related Research. doi:10.15308/Sinteza-2019-445-451
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