Image denoising by additive fog light using a deep dictionary – We present a method for image denoising with two fundamental components: a global and a local model. Compared to previous methods which have been presented on this problem, we show that it is possible to extend such a representation to new problems and still achieve satisfactory results without resorting to expensive dictionary-based denoising techniques. Here we show how the deep learning method can be used to encode the global model to represent the image. Since the global model is not directly present in the denoised data, this new representation is robust to noise and can be easily learnt without expensive dictionary-based denoising. Our main experimental and theoretical results demonstrate that the proposed method outperformed the existing methods on datasets containing only noise. Finally, we show that the proposed loss function is equivalent to the full loss, even when the image is cropped only from the global model. Our experiments demonstrate the effectiveness of the proposed network for image denoising.

In this paper, we propose a novel neural generative model (GAN), which can take multiple models (for which only one can generate an image) and iteratively update them simultaneously, without any prior knowledge of the source of each model. The generative models have a natural image model structure, meaning they can generate images from the given input image and infer the object model from the learned model data. In this paper, we are trying to use the learning principle of the GAN to learn the model structure. We propose to use the learned model as a nonlinear model and train it only in the model data. This model is not restricted to the given image data, but instead can be adapted to generate a new image from a given image. The model is used to provide predictions of the model with respect to a given source image. The model does not need additional parameters nor any other information in order to learn the model. Our method can be used for image classification tasks such as image retrieval, image search, and image annotation applications, which requires very large training sets.

An Adaptive Regularization Method for Efficient Training of Deep Neural Networks

The Effect of Differential Geometry on Transfer Learning

Image denoising by additive fog light using a deep dictionary

Deep Multitask Learning for Modeling Clinical Notes

Convolution, Sweeping and Residualization Techniques for Unsupervised Image Annotation with Neural NetworksIn this paper, we propose a novel neural generative model (GAN), which can take multiple models (for which only one can generate an image) and iteratively update them simultaneously, without any prior knowledge of the source of each model. The generative models have a natural image model structure, meaning they can generate images from the given input image and infer the object model from the learned model data. In this paper, we are trying to use the learning principle of the GAN to learn the model structure. We propose to use the learned model as a nonlinear model and train it only in the model data. This model is not restricted to the given image data, but instead can be adapted to generate a new image from a given image. The model is used to provide predictions of the model with respect to a given source image. The model does not need additional parameters nor any other information in order to learn the model. Our method can be used for image classification tasks such as image retrieval, image search, and image annotation applications, which requires very large training sets.