Recurrent Inference by Mixture Models


Recurrent Inference by Mixture Models – This paper proposes a new deep convolutional neural network (CNN) to learn a model for the detection and learning of semantic features. Our approach is based on an inverted recurrent neural network (RNN) which contains two layers with a hidden layer. We propose to divide the RNN into two groups, using an inverted recurrent NN (or recurrent LNN) which learns representations of the hidden layer. This architecture is capable of learning representations of the hidden layer given that it is well-trained. We also propose an architecture to learn semantic features using a supervised learning framework based on convolutional neural network (CNN). Our method can be used as a basis for designing neural models, so that visual features can be learned easily from the RNN. Here, the visual features will be learned by RNNs.

Most of the state-of-the-art detection based image recognizers in image-centric applications are based on unsupervised deep learning algorithms, i.e. they learn to match images at different scales while maintaining low accuracy. In this paper, we propose an unsupervised image-centric image detection system that provides significantly increased accuracy in detecting images of different sizes and orientations without affecting the model’s classification accuracy. Our approach achieves a significant reduction in the performance of the system by leveraging the model’s knowledge. To our knowledge this is the first unsupervised deep learning based image recognition system for the purpose of unsupervised image segmentation for high spatial and intra-scale. Experimental evaluations of our system on three benchmark datasets demonstrate the effectiveness of our unsupervised detection model compared to state-of-the-art systems on two image datasets.

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Recurrent Inference by Mixture Models

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  • Learning to Find and Recommend Similarities Across Images and Videos

    Practical Residual Learning for Image RecognitionMost of the state-of-the-art detection based image recognizers in image-centric applications are based on unsupervised deep learning algorithms, i.e. they learn to match images at different scales while maintaining low accuracy. In this paper, we propose an unsupervised image-centric image detection system that provides significantly increased accuracy in detecting images of different sizes and orientations without affecting the model’s classification accuracy. Our approach achieves a significant reduction in the performance of the system by leveraging the model’s knowledge. To our knowledge this is the first unsupervised deep learning based image recognition system for the purpose of unsupervised image segmentation for high spatial and intra-scale. Experimental evaluations of our system on three benchmark datasets demonstrate the effectiveness of our unsupervised detection model compared to state-of-the-art systems on two image datasets.


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