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Article
Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification
Real-world data are long-tailed, the lack of tail samples leads to a significant limitation in the generalization ability of the model. Although numerous approaches of class re-balancing perform well for moder...
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Article
Knowledge distillation-based performance transferring for LSTM-RNN model acceleration
The sequence data processing, such as signal classification, is an important part of pattern recognition. Long short-term memory recurrent neural networks (LSTM-RNN) are widely applicable across the sequence d...
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Article
Depth map super-resolution based on edge-guided joint trilateral upsampling
Depth image super-resolution (DISR) is a significant yet challenging task. In this paper, we propose a novel edge-guided framework for color-guided DISR aiming at reducing the artifacts caused by the introduce...
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Article
Open AccessPansharpening based on convolutional autoencoder and multi-scale guided filter
In this paper, we propose a pansharpening method based on a convolutional autoencoder. The convolutional autoencoder is a sort of convolutional neural network (CNN) and objective to scale down the input dimens...
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Article
Co-learning saliency detection with coupled channels and low-rank factorization
In this paper, a co-learning saliency detection method is proposed via coupled channels and low-rank factorization, by imitating the structural sparse coding and cooperative processing mechanism of two dorsal ...
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Article
Deep geometric convolutional network for automatic modulation classification
A recent trend of automatic modulation classification is to automatically learn high-level abstraction of signals, instead of manually designing features for further classification. In this paper, we propose a...