On the Performance of Deep Convolutional Neural Networks in Semi-Supervised Learning of Point Clouds


On the Performance of Deep Convolutional Neural Networks in Semi-Supervised Learning of Point Clouds – The success of deep learning is due to its ability to predict large number of points (and thus learning the entire model). We propose a novel Deep Convolutional Neural Network model that is able to learn the feature vectors of the underlying deep learning network, while retaining the ability to capture small number of point clouds. We provide a novel deep learning model for complex scene object classification and object segmentation, leveraging its ability to learn feature vectors, both at multiple scales. To this end, we train Deep Neural Network (DNN) models by using sparse models, which is an important technique in a wide range of practical applications. The proposed Deep Convolutional Network (DCNN) captures many important information within the convolutional layers, whereas the sparse model captures the whole scene at multiple scales and allows to scale to the larger number of points. We experimentally demonstrate that our proposed DCNN model achieves faster classification accuracy compared to state-of-the-art deep learning methods, in a large number of realistic scenes with a large number of hidden objects and many other objects.

We present an end-to-end model-based algorithm to encode and extract the semantic meanings of sentences. By extracting a semantic meaning from a sequence of sentences, we aim to capture the semantic structure in a graph and propose a method for learning from the set of sentences. Since the semantic meaning of sentences are expressed through a graph, we propose a novel, discriminative representation for these sentences using deep graph models (DNNs). Experiments using a novel dataset (Gibson Bayes dataset) and several supervised learning tasks, both in a real-world data set, have revealed that the proposed architecture achieves state-of-the-art accuracies on language segmentation.

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On the Performance of Deep Convolutional Neural Networks in Semi-Supervised Learning of Point Clouds

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  • A Stochastic Non-Monotonic Active Learning Algorithm Based on Active Learning

    Improving Recurrent Neural Networks with GraphsWe present an end-to-end model-based algorithm to encode and extract the semantic meanings of sentences. By extracting a semantic meaning from a sequence of sentences, we aim to capture the semantic structure in a graph and propose a method for learning from the set of sentences. Since the semantic meaning of sentences are expressed through a graph, we propose a novel, discriminative representation for these sentences using deep graph models (DNNs). Experiments using a novel dataset (Gibson Bayes dataset) and several supervised learning tasks, both in a real-world data set, have revealed that the proposed architecture achieves state-of-the-art accuracies on language segmentation.


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