Hussam Qassim

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One of the most investigated methods to increase the accuracy of convolutional neural networks (CNN) is by increasing its depth. However, increasing the depth also increases the number of parameters, which makes convergence of backpropagation very slow and prone to overfitting. Convolutional networks with deep supervision (CNDS) add auxiliary branch to(More)
Convolutional Neural Networks nowadays are of tremendous importance for any image classification system. One of the most investigated methods to increase the accuracy of CNN is by increasing the depth of CNN. Increasing the depth by stacking more layers also increases the difficulty of training besides making it computationally expensive. Some research(More)
Deep learning has given way to a new era of machine learning, apart from computer vision. Convolutional neural networks have been implemented in image classification, segmentation and object detection. Despite recent advancements, we are still in the very early stages and have yet to settle on best practices for network architecture in terms of deep design,(More)
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