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The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen...
The huge training overhead for obtaining channel state information (CSI) at the BS has been recognized as a major challenge in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) cellular networks. To solve this problem, we propose an angular domain pilot design and channel estimation scheme to reduce the required overhead by exploiting the angle domain channel sparsity....
We propose to leverage concept-level representations for complex event recognition in photographs given limited training examples. We introduce a novel framework to discover event concept attributes from the web and use that to extract semantic features from images and classify them into social event categories with few training examples. Discovered concepts include a variety of objects, scenes, actions...
Context-based method for classification has been successfully applied in image. However, most of these classifiers work in stages. This paper presents a novel discriminative model named context-based max-margin to perform the task of classification for polarimetric synthetic aperture radar (PolSAR) images. Based on the max-margin frame, support vector machine (SVM), and conditional random fields (CRF)...
A new method for Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification based on Deep Sparse Filtering Network (DSFN) is proposed in this paper. It uses a novel deep learning network to learn features from the input raw data automatically. And the spatial information between pixels on PolSAR image is combined into the input data. Moreover, unlike the conventional deep networks, the...
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