Learning the Parameters of Deep Convolutional Networks with Geodesics


Learning the Parameters of Deep Convolutional Networks with Geodesics – In this paper, a non-parametric method to measure the uncertainty of the neural network model can be presented. To this end, we study how to accurately predict the posterior distribution of the neural network models. We compare the posterior distribution of the neural network models with a standard Gaussian model for different applications. Our results, comparing three different nonparametric approaches, show that a neural network model may show more uncertainty than the Gaussian model for different real-world applications.

Deep neural networks (DNN) have been successfully used to model and classify objects on a wide-scale dataset. Their performance has been consistently improved on several object-related datasets. However, they exhibit some weaknesses. In particular, their ability to learn sparse structures has been severely hindered. In this paper, we take a challenging approach to learn embeddings of objects on an actual visual dataset and combine the results learned from previous algorithms to better learn object-related structures. We develop a network classifier that can predict objects on the ground truth given a set of object categories. Our model, dubbed Multi-Object Categorized Localization (MOCAL), achieves global classification accuracy of 0.89 on the PASCAL VOC dataset, compared to 0.88 on the best previous classifier, CNN. Moreover, it can successfully generate fine-grained images of objects with complex appearance features. We observe that our model is effective at successfully learning object-related structures on an actual visual dataset, but has limitations on its ability of learning rich structural representations.

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Learning the Parameters of Deep Convolutional Networks with Geodesics

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    A Deep Learning Method for Optimal Vehicle LocationDeep neural networks (DNN) have been successfully used to model and classify objects on a wide-scale dataset. Their performance has been consistently improved on several object-related datasets. However, they exhibit some weaknesses. In particular, their ability to learn sparse structures has been severely hindered. In this paper, we take a challenging approach to learn embeddings of objects on an actual visual dataset and combine the results learned from previous algorithms to better learn object-related structures. We develop a network classifier that can predict objects on the ground truth given a set of object categories. Our model, dubbed Multi-Object Categorized Localization (MOCAL), achieves global classification accuracy of 0.89 on the PASCAL VOC dataset, compared to 0.88 on the best previous classifier, CNN. Moreover, it can successfully generate fine-grained images of objects with complex appearance features. We observe that our model is effective at successfully learning object-related structures on an actual visual dataset, but has limitations on its ability of learning rich structural representations.


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