Object Tracking in the Wild: A Benchmark for Feature Extraction


Object Tracking in the Wild: A Benchmark for Feature Extraction – Recently a key issue when using deep networks for facial recognitions has to be considered: the accuracy of the recognition metrics when the network model is trained only on the image-level image patches. In this paper, we propose to use deep networks to improve the recognition performance by maximizing the classification accuracy of the feature vectors. Specifically, we adopt an unsupervised learning method which computes the similarity between feature vectors and their normalized vectors, and we then use a deep learning architecture to train it. We show that our approach leads to significant improvements over the baseline in classification accuracy compared with a traditional CNN+DCT system and also lead to a significant increase in feature recognition rate. Our deep network architectures are trained with a simple model on MNIST image patches. We also demonstrate that the learned feature vectors with large training set can be better used to estimate the recognition accuracy of the feature vectors, compared to a simple CNN method.

We propose a novel method for predicting nonconvex nonlinear function from a high-dimensional data, a task that has recently received great attention from computer science and artificial intelligence researchers. This paper presents a deep learning approach to predict nonconvex function from high-dimensional data using two complementary learning algorithms. On the one hand, we first propose and demonstrate a new method for predicting nonconvex functions from high-dimensional data, which is based on Gaussianity Networks, which is known to be difficult to learn in practice. On the other hand, a simple regularization method based on nonlinearity networks over linear discriminant distributions is proposed to achieve better prediction performance on all these functions. Using the proposed method, we are able to learn complex regularization rules over all the functions with respect to the data, and improve the training results of the model in various cases.

Sparse and Robust Principal Component Analysis

Efficient Inference for Multi-View Bayesian Networks

Object Tracking in the Wild: A Benchmark for Feature Extraction

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  • Understanding a learned expert system: design, implement and test

    Learning Visual Probabilistic Models from Low-Grade Imagery with Deep Learning – A Deep Reinforcement Learning ApproachWe propose a novel method for predicting nonconvex nonlinear function from a high-dimensional data, a task that has recently received great attention from computer science and artificial intelligence researchers. This paper presents a deep learning approach to predict nonconvex function from high-dimensional data using two complementary learning algorithms. On the one hand, we first propose and demonstrate a new method for predicting nonconvex functions from high-dimensional data, which is based on Gaussianity Networks, which is known to be difficult to learn in practice. On the other hand, a simple regularization method based on nonlinearity networks over linear discriminant distributions is proposed to achieve better prediction performance on all these functions. Using the proposed method, we are able to learn complex regularization rules over all the functions with respect to the data, and improve the training results of the model in various cases.


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