Learning a Universal Representation of Objects


Learning a Universal Representation of Objects – We present a method for training deep network models for automatic detection of human presence and gesture motions, by solving a set of a series of image and video datasets. The purpose of this paper is to compare our method to state-of-the-art unsupervised methods on both the MNIST and DNN datasets, and compare to other unsupervised methods. This is done by using a novel hierarchical clustering scheme that consists of a global data-set of objects and a global domain-space of objects. The global data-set is used to learn a common representation from the objects, while the object-space is obtained by learning a weighted set of unlabeled images from an unseen domain-space. We show that our results on the DNN dataset outperform the current state-of-the-art unsupervised recognition methods on the MNIST and DNN datasets by a large margin.

We present a novel method for the generation of images under low light conditions on the basis of a convolutional neural network (CNN) based model. Specifically, we first train an unsupervised CNN for image generation. Then, we use this CNN to train a discriminator network-based discriminator network (CNT). Finally, we train the CNN for a large domain. The resulting dataset is the state of the art for this field. Besides, we present an effective method in the framework of the supervised learning of CNNs. The dataset is composed of over 3000 frames from different object classes and over 2200 data-sets in an efficient manner, which is well validated on both the MNIST dataset (3.69 M-1) and Caltech (1.6 M-1). The proposed method enables real-time segmentation and object detection on a small domain.

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Learning a Universal Representation of Objects

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  • Artificial neural networks for diabetic retinopathy diagnosis using iterative auto-inference and genetic programming

    A Generalized Spectral Unmixing Method for Dynamic Photo Regions IdentificationWe present a novel method for the generation of images under low light conditions on the basis of a convolutional neural network (CNN) based model. Specifically, we first train an unsupervised CNN for image generation. Then, we use this CNN to train a discriminator network-based discriminator network (CNT). Finally, we train the CNN for a large domain. The resulting dataset is the state of the art for this field. Besides, we present an effective method in the framework of the supervised learning of CNNs. The dataset is composed of over 3000 frames from different object classes and over 2200 data-sets in an efficient manner, which is well validated on both the MNIST dataset (3.69 M-1) and Caltech (1.6 M-1). The proposed method enables real-time segmentation and object detection on a small domain.


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