Robust Learning of Spatial Context-Dependent Kernels


Robust Learning of Spatial Context-Dependent Kernels – We investigate the use of latent variable models to train a machine-learned model to predict the location of objects. It is generally defined as a nonlinear network structure, and the network structure often consists of a fixed number of variables. In this paper, we model the network structure of a latent variable model and show that the network structure, in the latent space, is important to the learning task. We model the network structure of the model, which consists of one feature, multiple variables, and a fixed dimensionality measure (e.g., k-fold weight). The dimensionality measure is used to infer which variable is most relevant for the model. Extensive evaluation on both synthetic and real data shows that the proposed algorithm obtains superior performance in the real world. Experiments on ImageNet and BIDS demonstrate that the proposed algorithm consistently produces superior results compared to the state of the art.

A recently proposed method for unsupervised translation (OSMT) is based on the idea of learning a deep neural network to translate objects by identifying the regions in which they should be localized. The OSMT algorithm learns the region that best localizes the object and then translates the object by means of a recurrent neural network. The underlying feature sets are learned from the model, and hence the proposed OSMT method learns the representation of the objects in the feature set at hand. We demonstrate that the proposed method outperforms state-of-the-art unsupervised translation methods on an OSMT task.

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Robust Learning of Spatial Context-Dependent Kernels

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  • Probabilistic Learning and Sparse Visual Saliency in Handwritten Characters

    Feature Selection with Generative Adversarial Networks Improves Neural Machine TranslationA recently proposed method for unsupervised translation (OSMT) is based on the idea of learning a deep neural network to translate objects by identifying the regions in which they should be localized. The OSMT algorithm learns the region that best localizes the object and then translates the object by means of a recurrent neural network. The underlying feature sets are learned from the model, and hence the proposed OSMT method learns the representation of the objects in the feature set at hand. We demonstrate that the proposed method outperforms state-of-the-art unsupervised translation methods on an OSMT task.


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