Improving Human Parsing by Exploiting Minimal Metric Accuracy in Deep Neural Networks


Improving Human Parsing by Exploiting Minimal Metric Accuracy in Deep Neural Networks – We develop a novel deep learning framework to automatically segment the human brain into regions of high predictive utility. Our model has been trained on the MNIST dataset and can outperform state-of-the-art deep models on both the image classification task and the recognition task. To better understand the structure of the brain, we also propose to map the entire brain into a network of neurons. We show that using a CNN-based model is a significant improvement over existing techniques and outperforms the existing approaches on the recognition task using MNIST data from the Stanford ImageNet 2017 task.

Recently, many methods have been proposed to improve the precision of the semantic segmentation task. In this paper, two approaches are proposed to reduce the computational cost in semantic segmentation. First, a fast LSTM (Log2vec) classifier is employed by the algorithm that uses LSTMs as the input. A deep learning algorithm is used to train this classifier. In addition, a distance measure is devised to measure the precision. For all tested algorithms, the proposed method achieves a 95.99% accuracy on semantic segmentation task.

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Improving Human Parsing by Exploiting Minimal Metric Accuracy in Deep Neural Networks

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    Fast Low-Rank Matrix Estimation for High-Dimensional Text ClassificationRecently, many methods have been proposed to improve the precision of the semantic segmentation task. In this paper, two approaches are proposed to reduce the computational cost in semantic segmentation. First, a fast LSTM (Log2vec) classifier is employed by the algorithm that uses LSTMs as the input. A deep learning algorithm is used to train this classifier. In addition, a distance measure is devised to measure the precision. For all tested algorithms, the proposed method achieves a 95.99% accuracy on semantic segmentation task.


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