Deep Neural Networks for Stochastic Optimization via Robust Estimation


Deep Neural Networks for Stochastic Optimization via Robust Estimation – We present a technique to classify noisy object images at low-level semantic similarity, based on the notion of unsupervised feature-level classification. We analyze the two main features of images: their spatial resolution and their intrinsic similarity. Then, we design a neural network algorithm that can efficiently and accurately predict their semantic similarity according to their spatial resolution with strong training in terms of both their geometric and spatial similarity. Such a method effectively combines both spatial resolution and visual similarity in the training data. To show their effectiveness and their superiority, we compare their performance to a deep learning based semantic image retrieval method. We show that the proposed method significantly outperforms both techniques for both classification and categorization tasks. In addition to this, we further propose a deep convolutional neural network architecture to learn the semantic cues from images. The proposed framework is able to perform semantic classification and categorization tasks effectively for both semantic and spatial information in image datasets.

This paper shows that a simple linear discriminantial method, based on a non-overlapping filter, can be more robust to noisy input signals while keeping high discriminative power in image domain. In this paper, we describe the method and compare it with several other discriminantial approaches. The method is evaluated on four widely used benchmark datasets with different classification rates. The results showed that our method achieves better results than the previous state-of-the-art methods for classification accuracy in the class of noisy images of an unknown size.

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Deep Neural Networks for Stochastic Optimization via Robust Estimation

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  • An Instance Segmentation based Hybrid Model for Object Recognition

    Deep Learning for ClassificationThis paper shows that a simple linear discriminantial method, based on a non-overlapping filter, can be more robust to noisy input signals while keeping high discriminative power in image domain. In this paper, we describe the method and compare it with several other discriminantial approaches. The method is evaluated on four widely used benchmark datasets with different classification rates. The results showed that our method achieves better results than the previous state-of-the-art methods for classification accuracy in the class of noisy images of an unknown size.


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