A Deep Learning Method for Optimal Vehicle Location


A Deep Learning Method for Optimal Vehicle Location – 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.

We propose to efficiently train recurrent multi-task learning models by using random walk (RK), and show that these models are more reliable and perform more efficiently than the state of the art. We apply our RK model to the learning of large-scale multilayer perceptrons, show that it outperforms other state of the art models.

The Fuzzy Matrix

Highlighting spatiotemporal patterns in time series with CNNs

A Deep Learning Method for Optimal Vehicle Location

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  • A New Model of Semantic Understanding for Video Summarization

    Fast Task Selection via Recurrent Residual NetworksWe propose to efficiently train recurrent multi-task learning models by using random walk (RK), and show that these models are more reliable and perform more efficiently than the state of the art. We apply our RK model to the learning of large-scale multilayer perceptrons, show that it outperforms other state of the art models.


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