Convolutional Neural Network (CNN) for Image Detection and Recognition
By Rahul Chauhan, Kamal Kumar Ghanshala, Ramesh Chandra Joshi. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018
500 citations recorded by the source corpus.
Deep Learning algorithms are designed in such a way that they mimic the function of the human cerebral cortex. These algorithms are representations of deep neural networks i.e. neural networks with many hidden layers. Convolutional neural networks are deep learning algorithms that can train large datasets with millions of parameters, in form of 2D images as input and convolve it with filters to produce the desired outputs. In this article, CNN models are built to evaluate its performance on image recognition and detection datasets. The algorithm is implemented on MNIST and CIFAR-10 dataset and its performance are evaluated. The accuracy of models on MNIST is 99.6 %, CIFAR-10 is using real-time data augmentation and dropout on CPU unit.
Subfields: Computer Vision and Pattern Recognition, Neurology
View Paper: https://www.semanticscholar.org/paper/b4eed12147fabac8f670ed7443eda90926a0d6ad
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Citations in the corpus
- Federated Learning in Edge Computing: A Systematic Survey (2022, methodology): “The key idea is to randomly drop out the coordinates of the stochastic gradient vectors and amplify the remaining coordinates appropriately to ensure the sparsified gradient [163] is unbiased.”
- Deep learning in drug discovery: an integrative review and future challenges (2022, unknown): “Image recognition, image analysis, image segmentation, video analysis, and natural language processing (NLP) (Chauhan et al. 2018; Tajbakhsh et al. May 2016; Mohamed et al. 2020; Zhang et al. 2018) are among the tasks that CNNs are capable of.”