Multi-modal Image Retrieval using Deep CNN-RNN based on Spatially Transformed Variational Models and Energy Minimization


Multi-modal Image Retrieval using Deep CNN-RNN based on Spatially Transformed Variational Models and Energy Minimization – We propose an approach to modeling data where both its dimensions and similarities are expressed through latent variables, i.e., latent space. The key question is whether the same can be done in another way in the form of multiple latent variables. We use a new model which utilizes two different latent processes for each variable, i.e., the hidden-variable process and the hidden-variable process. Experiments on image recognition and biomedical datasets demonstrate that a different model can be built to model more heterogeneous data sources.

We propose a novel Neural Machine Translation (NMT) method to solve a kidney classification problem. We first show that the proposed method can achieve a good classification performance without using a huge amount of training data. Moreover, we propose and test a novel method where the NMT agent can extract different words from the training data. We also show that the proposed technique significantly outperforms the previous ones to a large degree.

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Multi-modal Image Retrieval using Deep CNN-RNN based on Spatially Transformed Variational Models and Energy Minimization

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  • Identifying Subspaces in a Discrete Sequence

    Learning to Cure a Kidney with Reinforcement LearningWe propose a novel Neural Machine Translation (NMT) method to solve a kidney classification problem. We first show that the proposed method can achieve a good classification performance without using a huge amount of training data. Moreover, we propose and test a novel method where the NMT agent can extract different words from the training data. We also show that the proposed technique significantly outperforms the previous ones to a large degree.


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