Action Recognition with 3D CNN: Onsets and Transformations


Action Recognition with 3D CNN: Onsets and Transformations – Recently, the success of deep learning and deep learning-based generative adversarial neural networks (GANs) have led to a plethora of potential applications of the machine learning method for this task. In this paper, we investigate the possibility of deploying CNNs into a variety of applications; from learning to recognize faces using images of faces with different faces to pose-based models leveraging motion capture for face recognition. First we build a CNN trained to recognize faces from a large amount of data from a public database. After that, the CNN trained to solve a pose-based pose retrieval system on images of faces which are difficult to obtain at face recognition time. We demonstrate that our method successfully retrieved 3,732 face images of 8,929 subjects from the public public database. After extracting 3,767 face images from the database, our method is able to obtain a high recognition rate and successfully achieve good recognition rates. Our method significantly outperforms recent state-of-the-art pose and pose retrieval method.

We present a novel method for learning supervised learning problems based on an adversarial learning algorithm. The adversarial technique is motivated by the fact that it is the least-squares model used in practice. The approach exploits the adversarial learning principle to minimize the influence of its adversarial input and to reduce the adversarial output to a set of small, weighted minimizers. The objective is to minimize the total variance of the squared loss function over adversarial input and minimizes the adversarial output. We apply our learned adversarial algorithm to various supervised learning tasks, including classification, clustering, and classification with a single pass of the training images. Our results show that the proposed approach provides a simple yet effective learning technique to improve both prediction accuracy and performance. Using this approach, we found that the proposed approach significantly outperforms competing methods on three datasets.

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Action Recognition with 3D CNN: Onsets and Transformations

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  • FractalGradient: Learning the Gradient of Least Regularized Proximal Solutions

    Video In HV range prediction from the scientific literatureWe present a novel method for learning supervised learning problems based on an adversarial learning algorithm. The adversarial technique is motivated by the fact that it is the least-squares model used in practice. The approach exploits the adversarial learning principle to minimize the influence of its adversarial input and to reduce the adversarial output to a set of small, weighted minimizers. The objective is to minimize the total variance of the squared loss function over adversarial input and minimizes the adversarial output. We apply our learned adversarial algorithm to various supervised learning tasks, including classification, clustering, and classification with a single pass of the training images. Our results show that the proposed approach provides a simple yet effective learning technique to improve both prediction accuracy and performance. Using this approach, we found that the proposed approach significantly outperforms competing methods on three datasets.


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