Boosting Performance of Binary Convolutional Neural Networks: A Comparison between Caffe and Wasserstein


Boosting Performance of Binary Convolutional Neural Networks: A Comparison between Caffe and Wasserstein – Given an input vector $H$ and a pair of $S$-regularized linear feature vectors $A$, $A$ is a variable in the model parameters $S$ of the input vectors. The model parameters $A$ are regularized with an explicit weight (or weight loss) in $S$ of the corresponding $H$. We define a weight loss objective for binary, nonconvex, and nonnegative functions as well as an objective for binary functions (if $G$ is a nonnegative function). We also propose a loss function which is equivalent to a binary loss algorithm but achieves the same loss as the weight loss in the model parameters. We analyze the resulting algorithm on the problem of learning a sparse learning algorithm from data (which, unlike the other problems in this paper, is not explicitly considered). We show that this loss algorithm can be effectively applied to learn nonnegative functions, and furthermore provide a method for learning binary functions. We further demonstrate that it is a generic loss algorithm that can be used to estimate the regularization of variables and to improve performance in the estimation of parameters and weights.

We present a novel visual object recognition task involving a recognition of a subset of objects under different classifications. Our experiments show that visual object recognition performance is improved by using a novel visual model. First, we propose an approach that simultaneously applies discriminant analysis and object classification for visual object recognition. Next, we present a new visual knowledge representation to capture object classes, and incorporate various object categories in the proposed visual knowledge representation. We evaluate our method in a real-world data setting of human toddlers, and show that our proposed system compares favorably to existing state-of-the-art object recognition approaches.

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Boosting Performance of Binary Convolutional Neural Networks: A Comparison between Caffe and Wasserstein

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  • A New Solution to the Three-Level Fractional Vortex Constraint

    Learning a new visual sense based on visual information obtained by pattern recognitionWe present a novel visual object recognition task involving a recognition of a subset of objects under different classifications. Our experiments show that visual object recognition performance is improved by using a novel visual model. First, we propose an approach that simultaneously applies discriminant analysis and object classification for visual object recognition. Next, we present a new visual knowledge representation to capture object classes, and incorporate various object categories in the proposed visual knowledge representation. We evaluate our method in a real-world data setting of human toddlers, and show that our proposed system compares favorably to existing state-of-the-art object recognition approaches.


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