diffGrad: An Optimization Method for Convolutional Neural Networks
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..., 2019c) DiffGrad (Dubey et al., 2020) SADAM (Tong et al....
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...The optimal learning rate [52] is chosen as 0....
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"diffGrad: An Optimization Method fo..." refers background or methods in this paper
...Adam computes adaptive learning rates for each parameter [38] by utilizing both first and second moments....
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...999, and learning rate α ∈ [10−2, 10−4] is a good starting choice for many models [38]....
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...The convergence property of Adam [38] is shown using the online learning framework proposed in [44]....
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...Adam [38] is another widely used gradient descent optimization technique that computes the learning rates at each step based on two vectors known as the 1 and 2 order moments (i....
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...In this experiment, for both Adam [38] as well as the proposed diffGrad optimization methods, the following are the hyper-parameter settings: the decay rate for 1 moment (β1) is 0....
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...The popular CNN architectures for image categorization problems are AlexNet [2], VGGNet [4], GoogleNet [19], and ResNet [20]....
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...Different CNN architectures have been proposed for image related problems such as AlexNet [2], VggNet [4], GoogLeNet [19], and ResNet [20] for image classification, R-CNN [21], Fast R-CNN [22], Faster R-CNN [23], and YOLO [24] for object detection, Mask R-CNN [25] and PANet [26] for instance segmentation, RCCNet [27] for colon cancer nuclei classification, etc....
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...Due to the availability of GPU-based highend computational facilities and the huge amount of data, deep learning based approaches generally outperform the traditional hand-designed approaches to solve research problems in Computer Vision [2], [3], [4], [5], Image Processing [6], [7], Signal Processing [8], [9], Robotics [10], Natural Language Processing [11], [12], and many other diverse areas of Artificial Intelligence....
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