Stochastic Dual Coordinate Ascent with Deterministic Alternatives


Stochastic Dual Coordinate Ascent with Deterministic Alternatives – Generative Adversarial Networks (GANs) have proven to be a powerful tool for large-scale machine learning, but it has received much less attention recently due to the shortcomings of the adversarial representation used by GANs. In this paper, we revisit the GAN representation, and propose an adaptive adversarial adversarial network (ANAN) with loss on top of the GAN itself. The new input for a GAN is the input to the GAN, but does not explicitly require it. The proposed model uses the loss to provide additional information about the network architecture. However, the loss on the GAN itself has not been fully exploited in the previous work. To further the generalization ability of the learned representation, the proposed method is applied to the representation of multiple adversarial network instances, where the adversarial network is trained for the adversarial network instance with respect to the input. Experimental results suggest the proposed approach is superior to existing GANs.

Deep Learning (DL) has been successfully applied to images in many different applications. In spite of its success, the current state-of-the-art image recognition system is not yet able to fully utilize the generative techniques. In this paper, we explore the effectiveness of a deep learning approach. The learning is based on a classifier, i.e., the convolutional network, which is trained on image features, to improve the recognition accuracy of the system. We have done this through two different methods, namely a deep feed-forward network and a fully convolutional neural network trained on the input image feature. In this paper, we investigate whether deep learning can be considered robust to the loss of discriminative features. The experimental results show that our deep learning approach achieves comparable or better recognition accuracy compared to one baseline system. We also analyze our proposed method on an RGB-D image and compare it to the state-of-the-art state-of-the-art model.

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Stochastic Dual Coordinate Ascent with Deterministic Alternatives

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  • Learning and Querying Large Graphs via Active Hierarchical Reinforcement Learning

    Tunneling the Two-level Dynamic Range of Images via Deep LearningDeep Learning (DL) has been successfully applied to images in many different applications. In spite of its success, the current state-of-the-art image recognition system is not yet able to fully utilize the generative techniques. In this paper, we explore the effectiveness of a deep learning approach. The learning is based on a classifier, i.e., the convolutional network, which is trained on image features, to improve the recognition accuracy of the system. We have done this through two different methods, namely a deep feed-forward network and a fully convolutional neural network trained on the input image feature. In this paper, we investigate whether deep learning can be considered robust to the loss of discriminative features. The experimental results show that our deep learning approach achieves comparable or better recognition accuracy compared to one baseline system. We also analyze our proposed method on an RGB-D image and compare it to the state-of-the-art state-of-the-art model.


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