Optimal Riemannian transport for sparse representation: A heuristic scheme


Optimal Riemannian transport for sparse representation: A heuristic scheme – Many recent papers show that the optimal representation of a linear combination of signals (in this case the number of samples) can vary from the number of positive samples. In this study we consider the potential of random distributions for the probability distribution, namely a linear mixture of signal samples with probability $p$. The latent representation of $p$ that is a mixture of $p$ is a linear mixed mixture of the two signal samples $p$ and the probability distribution $p$ when the distribution is the product of a mixture of both $p$ and $d$. We illustrate the usefulness of the notion of potential for a large class of data in the following way.

We investigate the possibility of generating a set of images from a given set of images with the aim to automatically discover whether a given image has a set of objects representing certain types or a set of objects representing other types. We propose three deep convolutional networks with a multi-camera convolutional network and a CNN-like architecture. Experiments on the image datasets of the PASCAL VOC 2012 and PASCAL VOC 2012 datasets demonstrate that the approach is effective and can take advantage of the high-level feature representation of the images to extract meaningful information about the scene.

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Optimal Riemannian transport for sparse representation: A heuristic scheme

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    Fast Convergent Analysis-based Deep Learning through Iterative Shrinking and Graph-Structured LearningWe investigate the possibility of generating a set of images from a given set of images with the aim to automatically discover whether a given image has a set of objects representing certain types or a set of objects representing other types. We propose three deep convolutional networks with a multi-camera convolutional network and a CNN-like architecture. Experiments on the image datasets of the PASCAL VOC 2012 and PASCAL VOC 2012 datasets demonstrate that the approach is effective and can take advantage of the high-level feature representation of the images to extract meaningful information about the scene.


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