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In this paper, we propose several novel deep learning methods for object saliency detection based on the powerful convolutional neural networks. In our approach, we use a gradient descent method to iteratively modify an input image based on the pixel-wise gradients to reduce a cost function measuring the class-specific objectness of the image. The(More)
Stochastic gradient descent (SGD) has been regarded as a successful optimization algorithm in machine learning. In this paper, we propose a novel annealed gradient descent (AGD) method for non-convex optimization in deep learning. AGD optimizes a sequence of gradually improved smoother mosaic functions that approximate the original non-convex objective(More)
Convolutional neural networks (CNNs) have yielded the excellent performance in a variety of computer vision tasks, where CNNs typically adopt a similar structure consisting of convolution layers, pooling layers and fully connected layers. In this paper, we propose to apply a novel method, namely Hybrid Orthogonal Projection and Estimation (HOPE), to CNNs in(More)
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