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5 days ago · Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. He, K., Zhang, X ...
4 days ago · Our method uses two Convolutional Neural Networks (CNNs) as candidate models of the ventral visual stream: the CORnet-S that has high neural predictivity in ...
2 days ago · Karen Simonyan (Google-DeepMind & Oxford) at ICLR 2015 Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan, Andrew Zisserman ...
5 days ago · This deep architecture allows the network to learn intricate patterns and representations, making it extremely powerful for image classification tasks. Detailed ...
Leveraging convolutional neural networks for efficient classification ...
link.springer.com › article
21 hours ago · Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning ...
4 days ago · Transfer learning is performed with pre-trained models, typically large Convolutional Neural Networks (CNNs) that are pre-trained on large standard benchmark ...
5 days ago · Karen Simonyan, Andrew Zisserman (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. In Proceedings of the 3rd International Confer-.
3 days ago · Convolutional neural networks. (CNNs) are a representative image recognition technology and one aspect of deep learning. In this study, we evaluated a CNN that ...
2 days ago · Convolutional neural networks (CNN) [11, 12] are often used in small-footprint KWS systems [2,3] to yield better performance with smaller models. Connectionist ...
A New Multi-objective Optimization Model for Optimal Configuration of ...
link.springer.com › chapter
4 days ago · In order to achieve great performance, CNNs have grown deeper, incorporating numerous layers. However, this depth results in a huge number of parameters, which ...