#EANF#


#EANF# –

We present a novel multi-view feature representation learning method for automatic segmentation of facial landmarks in images. We show that the proposed algorithm outperforms baseline approaches, with significant improvement of performance compared to the traditional approach. Additionally, we present a new benchmark dataset for automatically segmenting landmarks in images at human and machine levels using multi-view convolutional neural networks. Extensive evaluation on two standard benchmark datasets for facial landmarks segmentation shows that our framework significantly outperforms baseline approaches.

An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning Agents

A Hierarchical Latent Class Model for Nonlinear Dimensionality Reduction

#EANF#

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  • Fast Color Image Filtering Using a Generative Adversarial Network

    Multi-View Representation Lasso through Constrained Random Projections for Image RecognitionWe present a novel multi-view feature representation learning method for automatic segmentation of facial landmarks in images. We show that the proposed algorithm outperforms baseline approaches, with significant improvement of performance compared to the traditional approach. Additionally, we present a new benchmark dataset for automatically segmenting landmarks in images at human and machine levels using multi-view convolutional neural networks. Extensive evaluation on two standard benchmark datasets for facial landmarks segmentation shows that our framework significantly outperforms baseline approaches.


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