Efficient Non-Negative Ranking via Sparsity-Based Transformations


Efficient Non-Negative Ranking via Sparsity-Based Transformations – The problem of assigning labels to a class of objects has been gaining much interest in both scientific, engineering and machine learning applications. A special form of this question was considered when the labels of an object are not available or when they are not aligned. In this paper, we propose a novel method to deal with this issue using Sparsely Constrained Convolutional Neural Networks (SCNNs). In our framework, each node in a new object is represented as a pair of sparse, compressed and semi-transparent representations. To resolve the issue of labeling a new node, we propose to use a new CNN model for labeling the model instance and a new model on this instance. We further develop a sparsity-decorated CNN on a new instance to perform the labeling and discuss the usage of this model on various tasks, such as object recognition and segmentation.

Deep generative models (GANs) have attracted a lot of attention in recent years due to their potential and usefulness in the field of action-adversarial learning. GANs have traditionally been implemented as generative models with a deep network architecture built over some feature vectors. In this paper, we present a new method for learning a deep generative model (GAN) for indoor action recognition when using a set of latent representations. This method is based on learning a generative model over a dataset with the goal of modeling which objects are given from the dataset. The network is trained with a fully convolutional network to represent a set of latent representations of a target object. The network then learns a deep gAN. The learned model is referred to as the Deep GAN. We demonstrate that using the deep GAN in an indoor object recognition method significantly outperforms the other state-of-the-art methods in terms of the number of labeled objects over all types of instances.

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Efficient Non-Negative Ranking via Sparsity-Based Transformations

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  • On the Unnormalization of the Multivariate Marginal Distribution

    A Feature Based Deep Learning Recognition System For Indoor Action RecognitionDeep generative models (GANs) have attracted a lot of attention in recent years due to their potential and usefulness in the field of action-adversarial learning. GANs have traditionally been implemented as generative models with a deep network architecture built over some feature vectors. In this paper, we present a new method for learning a deep generative model (GAN) for indoor action recognition when using a set of latent representations. This method is based on learning a generative model over a dataset with the goal of modeling which objects are given from the dataset. The network is trained with a fully convolutional network to represent a set of latent representations of a target object. The network then learns a deep gAN. The learned model is referred to as the Deep GAN. We demonstrate that using the deep GAN in an indoor object recognition method significantly outperforms the other state-of-the-art methods in terms of the number of labeled objects over all types of instances.


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