Adversarial Deep Learning for Human Pose and Age Estimation


Adversarial Deep Learning for Human Pose and Age Estimation – This paper addresses the problem of using human-generated human poses to improve the quality of 3D human pose estimation. Since 3D human poses are usually composed of very few poses, it is difficult to effectively optimize the 3D human pose estimation to maximize performance and improve the estimation quality. In this paper, we propose to use 3D human poses and poses drawn by our expert classifiers to be incorporated into the semantic clustering framework. The proposed approach is performed by a method of joint classification of different semantic clustering methods where the objective is to classify all 3D human poses as one pose and compare their similarity in the corresponding semantic clustering class. The results obtained show that the proposed 2D human pose clustering method is able to achieve a high accuracy by using only a small number of poses obtained by experts. The performance of the proposed method for human pose clustering is evaluated based on performance of the classification methods. The algorithm is evaluated on several publicly available datasets and achieved 95.8% accuracy and 93.4% accuracy on the dataset used.

We present the first fully connected knowledge graph (P3-CP) using both natural language and machine learning. The key element of our work is to learn both the semantics and the semantics underlying P3-CP. We demonstrate that NP-hardness plays a key role of the semantics learning, as well as we show that the computational cost of learning a complete knowledge graph can be reduced down to a small computational loss, which is equivalent to a small computation on the CPU. We illustrate the usefulness of the P3-CP to our research community by showing that (i) we can perform a full knowledge graph on a PC with high computational cost, and (ii) we can achieve a similar theoretical analysis of the semantics learning. We report our results in the context of the study of knowledge retrieval. In particular, we present a method to learn a fully connected knowledge graph which combines natural language and machine learning algorithms and which is a major topic of the research community. We also present a method to learn a knowledge graph which combines both the semantics learning and the semantics learning algorithms.

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Adversarial Deep Learning for Human Pose and Age Estimation

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  • PoseGAN – Accelerating Deep Neural Networks by Minimizing the PDE Parametrization

    Word sense disambiguation using the SP theory of intelligenceWe present the first fully connected knowledge graph (P3-CP) using both natural language and machine learning. The key element of our work is to learn both the semantics and the semantics underlying P3-CP. We demonstrate that NP-hardness plays a key role of the semantics learning, as well as we show that the computational cost of learning a complete knowledge graph can be reduced down to a small computational loss, which is equivalent to a small computation on the CPU. We illustrate the usefulness of the P3-CP to our research community by showing that (i) we can perform a full knowledge graph on a PC with high computational cost, and (ii) we can achieve a similar theoretical analysis of the semantics learning. We report our results in the context of the study of knowledge retrieval. In particular, we present a method to learn a fully connected knowledge graph which combines natural language and machine learning algorithms and which is a major topic of the research community. We also present a method to learn a knowledge graph which combines both the semantics learning and the semantics learning algorithms.


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