Learning to Match for Sparse Representation of Images with Convolutional Neural Networks – This paper addresses the problem of image recognition using image compression. The problem involves recovering a compressed image from a low-quality, highly compressed image from intermediate frames. The compression problem stems from the fact that the compressed image contains noise, but a lower level of semantic information is available for processing. The problem consists in recovering a compressed image from a high-quality, highly compressed image for a low level of semantic information. This makes the approach to image retrieval difficult. In this work, we propose a novel low-level semantic retrieval approach using deep learning based architecture and we propose an unsupervised deep learning process to efficiently retrieve compressed images. The proposed approach has been trained on several publicly available datasets and compared to other successful deep-learners. The experimental results show that the proposed approach can efficiently retrieve high-quality images for training high-level semantic retrieval.

We propose a novel deep recurrent network architecture to build more complex neural networks by training its entire model independently from a single training data. We propose two separate layers, which are jointly trained to learn features of the input and learn representations, together with separate layers to control the model’s internal state and information content. Our two layers are compared against other state-of-the-art methods including ResNet, ConvNet, and ResNet. The state-of-the-art results demonstrate that the proposed architecture produces state-of-the-art results in terms of learning performance on many datasets, but not on the least of them, while in terms of learning rate on the most challenging datasets.

On the validity of the Sigmoid transformation for binary logistic regression models

Predicting Video Characteristics with Generative Adversarial Networks

Learning to Match for Sparse Representation of Images with Convolutional Neural Networks

Robust Detection of Microcalcification in Video Surveillance

Guaranteed Constrained Recurrent Neural Networks for Action RecognitionWe propose a novel deep recurrent network architecture to build more complex neural networks by training its entire model independently from a single training data. We propose two separate layers, which are jointly trained to learn features of the input and learn representations, together with separate layers to control the model’s internal state and information content. Our two layers are compared against other state-of-the-art methods including ResNet, ConvNet, and ResNet. The state-of-the-art results demonstrate that the proposed architecture produces state-of-the-art results in terms of learning performance on many datasets, but not on the least of them, while in terms of learning rate on the most challenging datasets.