Generalized Belief Propagation with Randomized Projections – Generative adversarial network (GAN) has received much attention recently.GAN has been shown to capture more information in the input images than other baselines and offers great success on many classification problems. However, the large number of classification datasets required to learn the underlying model has never been addressed in large datasets. This paper addresses this issue with Generative adversarial network (GAN) using a novel dataset structure called S-1-Mixture. A network is constructed with two branches where each branch contains all training data and the other branches contains data for classification. We use the two branches to separate the data and to extract the most relevant ones. The objective of the network is to achieve high classification accuracy and high classification speed in a large dataset with a high number of classification tasks. Experimental results on both public domain datasets demonstrate that the proposed method results in significant improvements over a state-of-the-art GAN model trained on publicly available datasets.

We propose a new method for machine learning. As a consequence, the learning algorithm can learn to encode complex knowledge representations in finite time. We show that the proposed method works with a limited number of parameters and achieves high performance when trained on a standard benchmark dataset. The performance of this method is further improved when it is applied to an on-line evaluation of the model, showing the significant performance gains obtained by our approach in both generalization and classification problems.

Deep learning with dynamic constraints: learning to learn how to look

A Fusion and Localization Strategy for the Visual Tracking of a Moving Object

Generalized Belief Propagation with Randomized Projections

Sparse and Optimal Sparsity

Bayesian Sparse Dictionary LearningWe propose a new method for machine learning. As a consequence, the learning algorithm can learn to encode complex knowledge representations in finite time. We show that the proposed method works with a limited number of parameters and achieves high performance when trained on a standard benchmark dataset. The performance of this method is further improved when it is applied to an on-line evaluation of the model, showing the significant performance gains obtained by our approach in both generalization and classification problems.