Learning from Negative News by Substituting Negative Images with Word2vec


Learning from Negative News by Substituting Negative Images with Word2vec – A new technique called negative image enhancement (NNE) has been proposed to exploit image attributes such as background, background color and foreground in a way that can increase the quality of a visual scene. However, only a limited amount of training data is available for the NNE approach. This paper proposes a novel approach based on the use of the image dimensionality score to enhance the quality of the image in a deep learning framework. We show that our proposed technique can effectively enhance the image in the same way as the image dimensionality score would enhance. The evaluation on several popular image enhancement benchmarks shows that our proposed method significantly improves performance compared to other similar approaches.

This work proposes a deep neural network (DNN) model for action prediction based on stochastic gradient descent. The method is based on three criteria, which includes (i) the presence of stochastic gradient decay and (ii) the fact that the stochastic and stochastic gradients are independent in the prediction stage. The proposed DNN model is trained end-to-end, and the trained DNN model is validated for the task of action prediction. In case of severe non-linearities in the prediction, the training data is taken from several datasets and the proposed DNN model is successfully trained end-to-end. Experimental results show that the proposed DNN model outperforms state-of-the-art on both MNIST and MSCAC 2007 benchmarks.

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Learning from Negative News by Substituting Negative Images with Word2vec

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  • Face Recognition with Generative Adversarial Networks

    Deep Learning for Precise Action PredictionThis work proposes a deep neural network (DNN) model for action prediction based on stochastic gradient descent. The method is based on three criteria, which includes (i) the presence of stochastic gradient decay and (ii) the fact that the stochastic and stochastic gradients are independent in the prediction stage. The proposed DNN model is trained end-to-end, and the trained DNN model is validated for the task of action prediction. In case of severe non-linearities in the prediction, the training data is taken from several datasets and the proposed DNN model is successfully trained end-to-end. Experimental results show that the proposed DNN model outperforms state-of-the-art on both MNIST and MSCAC 2007 benchmarks.


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