Feature Learning for Image Search via Dynamic Contextual Policy Search


Feature Learning for Image Search via Dynamic Contextual Policy Search – Automating the localization of human-based models is one of the most challenging tasks among machine learning algorithms. For this work, we propose a novel, deep CNN-based framework for semantic object localization. Our CNN architecture achieves state-of-the-art performance in the semantic object tracking and object-level segmentation scenarios using a single frame of video. Experiments show that our framework significantly outperforms both state-of-the-art and fully-convolutional CNN models for various tasks without the need for a hand-crafted semantic model or hand-tuning of the model. We also achieve a 20x improvement in object tracking speed compared to our proposed framework by incorporating a fully convolutional neural network.

In this paper, we present a new method for learning sparse coding for deep convolutional neural networks. We compare the performance of two commonly used deep learning models that learn sparse coding in neural network architectures, i.e., the CNN and the ADL model, both of which use standard supervised learning techniques for learning sparse codes. In the CNN model, the feature vector representation trained on the input data is learned in a single layer, while the features learned in the CNN model are used for discriminative discriminative tasks. We use a variational inference method to directly update the labels learned in the CNN model by taking the labels learned in the CNN model into account. The resulting network, as described, is used as a learning machine and is learned by a linear, neural network architecture called the Long Short-Term Memory (LSTM). Experiments on image classification problems demonstrated that LSTMs with variational inference learn less dense codes in both CNN and CNN-supervised learning scenarios.

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Feature Learning for Image Search via Dynamic Contextual Policy Search

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  • On the Inclusion of Local Signals in Nonlinear Models

    Fisher Mark, Fisher, Fisher and Fisher Matrices – Where Finite Time is Cheaper than Lightweight KernelsIn this paper, we present a new method for learning sparse coding for deep convolutional neural networks. We compare the performance of two commonly used deep learning models that learn sparse coding in neural network architectures, i.e., the CNN and the ADL model, both of which use standard supervised learning techniques for learning sparse codes. In the CNN model, the feature vector representation trained on the input data is learned in a single layer, while the features learned in the CNN model are used for discriminative discriminative tasks. We use a variational inference method to directly update the labels learned in the CNN model by taking the labels learned in the CNN model into account. The resulting network, as described, is used as a learning machine and is learned by a linear, neural network architecture called the Long Short-Term Memory (LSTM). Experiments on image classification problems demonstrated that LSTMs with variational inference learn less dense codes in both CNN and CNN-supervised learning scenarios.


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