Context-Aware Regularization for Deep Learning


Context-Aware Regularization for Deep Learning – We present a novel deep learning method to extract discriminative feature representations, using supervised learning methods. Deep Learning (DL) methods have often been used to extract complex discriminative features that represent complex relationships among objects, which have been recently exploited by Deep Learning (DL) methods for various classification problems. This motivates us to investigate the feasibility of deep learning based DL methods for a variety of problems as well as their practical use, to date. In this paper, we proposed a novel technique for learning the shape of an object from its parts, via the distribution of the objects’ parts, that is able to learn discriminative features, using supervised learning. By combining supervised learning techniques with visual input and model learning techniques, we further proposed a deep learning method using deep convolutional neural networks (DCNNs) to learn a 3D shape from a convolutional neural network (CNN). Experimental results show that the proposed DNN model outperformed conventional DCNN models in the supervised learning task when tested in image segmentation and human pose extraction.

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.

Adversarial Robustness and Robustness to Adversaries

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Context-Aware Regularization for Deep Learning

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  • Probabilistic programs in high-dimensional domains

    Face Recognition with Generative Adversarial NetworksIn 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.


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