Guaranteed Analysis and Model Selection for Large Scale, DNN Data


Guaranteed Analysis and Model Selection for Large Scale, DNN Data – We present a framework for automatically selecting the most relevant features from multiple images without any additional human intervention. Our method leverages two models of CNNs: the model that is the least informative, and the model that produces the most accurate model. In this work, we provide a general framework for automatically choosing the most relevant features of multiple CNNs that is applicable to arbitrary images, given the data which is sparse. Using a CNN with a low-dimensional latent representation, we propose a novel architecture for automatically choosing the relevant features for CNNs. The proposed selection method is based on the notion of context-invariant features (represented by spatial representations), and uses the spatial information to select the most relevant features that is needed to classify the image. We demonstrate the effectiveness of our proposal experiment by comparing our method with one from the literature: a supervised CNN that can learn to discriminate CNN features using just a single pixel of the input data. We demonstrate the effectiveness of the proposed approach by showing that classification is generally faster than the baseline approach and that it outperforms state-of-the-art feature selection methods.

Generative adversarial networks (GANs) have been successfully used for adversarial tracking in security applications. In this work, we propose a novel architecture for deep adversarial tracking. The architecture consists of two stages: (1) a stochastic adversarial network, which is conditioned on a data matrix containing the training samples, and (2) a fully-connected adversarial network, which is modeled as a convex matrix and is trained by a loss function. We show that the proposed scheme achieves the best performance with respect to all prior approaches.

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Guaranteed Analysis and Model Selection for Large Scale, DNN Data

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  • Learning to Reason with Imprecise Sensors for Object Detection

    A Bayesian Approach to Learning Deep Feature RepresentationsGenerative adversarial networks (GANs) have been successfully used for adversarial tracking in security applications. In this work, we propose a novel architecture for deep adversarial tracking. The architecture consists of two stages: (1) a stochastic adversarial network, which is conditioned on a data matrix containing the training samples, and (2) a fully-connected adversarial network, which is modeled as a convex matrix and is trained by a loss function. We show that the proposed scheme achieves the best performance with respect to all prior approaches.


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