Face Recognition with Generative Adversarial Networks


Face Recognition with Generative Adversarial Networks – In this paper, we present a novel neural network that can be described as a recurrent neural network in the sense that it is able to process millions of images simultaneously. We propose a novel end-to-end learning approach that is able to capture the underlying convolutional layers of the network, and is able to infer the semantic features of those images. The proposed approach combines a deep neural network based architecture with two novel deep recurrent networks (RNNs) to encode semantic information. RNNs consist of a recurrent layer, which is used to store semantic information, and a recurrent layer, which are connected through a neural network to encode the semantic information. This approach is also able to generate the semantic features while performing the inference of the image, which makes it easy to interpret them in practice. Experiments on the ImageNet, AVIUM and the KCCD datasets show that our approach is able to generate the semantic features of images accurately, with very rich semantic feature representations.

We present a method to improve the performance of video convolutional neural networks by maximizing the regret that a given CNN is able to recover due to its sparse representation. We propose a method to obtain this regret through the use of sparse features as input, which are learned by the loss function conditioned on the inputs. As a result, the weights in our network can be more efficiently recovered by applying a simple algorithm to a given loss function. The algorithm can be applied to video denoising, which is an important problem for machine learning applications, and can be viewed as a way to improve performance.

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Face Recognition with Generative Adversarial Networks

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  • Improving the Performance of $k$-Means Clustering Using Local Minima

    Sparse Convolutional Network Via Sparsity-Induced Curvature for Visual TrackingWe present a method to improve the performance of video convolutional neural networks by maximizing the regret that a given CNN is able to recover due to its sparse representation. We propose a method to obtain this regret through the use of sparse features as input, which are learned by the loss function conditioned on the inputs. As a result, the weights in our network can be more efficiently recovered by applying a simple algorithm to a given loss function. The algorithm can be applied to video denoising, which is an important problem for machine learning applications, and can be viewed as a way to improve performance.


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