Efficient Convolutional Neural Network Classifier – In this paper, we propose the concept of convolutional networks to automatically classify images. In general, we focus on the classification of images that contain the missing information for image classification, and then apply convolutional networks for learning the missing information to obtain better classification results. Our experiments show that training a convolutional network with training data from a single image and an ensemble of convolutional inputs outperforms the training network only with the same number of parameters. Additionally, we propose a novel method to learn the feature representations associated with the two images, which has an efficient model for the classification of missing image.
This paper proposes the first novel image summarization framework for Deep Neural Networks, which is an improvement over the current state-of-the-art and does not require any prior knowledge of deep models. This is especially true for image retrieval with a large dataset, the recently released MNIST dataset. We present an implementation of our state-of-the-art Image Retrieval Framework (IRF) to solve the Image Retrieval Problems (IRP). Our framework can simultaneously learn and execute the Retrieval algorithms, by leveraging the knowledge learned by deep models. It can be applied to various tasks and datasets. The implementation can be implemented as end-to-end parallel and has a new architecture implemented using the MNIST dataset.
Probability Sliding Curves and Probabilistic Graphs
Lasso-GANs for Regularized Linear Regression and Multilabel Classification
Efficient Convolutional Neural Network Classifier
Crowdsourced Content-based Image Retrieval using Deep Learning and Constrained Codebook TrainingThis paper proposes the first novel image summarization framework for Deep Neural Networks, which is an improvement over the current state-of-the-art and does not require any prior knowledge of deep models. This is especially true for image retrieval with a large dataset, the recently released MNIST dataset. We present an implementation of our state-of-the-art Image Retrieval Framework (IRF) to solve the Image Retrieval Problems (IRP). Our framework can simultaneously learn and execute the Retrieval algorithms, by leveraging the knowledge learned by deep models. It can be applied to various tasks and datasets. The implementation can be implemented as end-to-end parallel and has a new architecture implemented using the MNIST dataset.