A Byproduct of a Differentiable Neural Network—Data Weighting from an Implicit Form to an Explicit Form

Part 4: Neural Computing and Swarm IntelligenceInternational audienceData weighting is important for data preservation and data mining. This paper presents a data weighting—neural network data weighting which obtains data weighting through transforming the implicit weighting of neural network to explicit weighting. This method includes two phases: in the first phase, choose a differentiable neural network whose transfer function is differentiable, and train the neural network on the ground of training samples; in the second phase, input the training samples as test samples into the network, calculate partial derivatives of the outputs with respect to inputs based on the differential characteristics of neural network, and statistical partial derivatives with respect to each input data item are used to calculate the weight of the data item. In this way, implicit weights stored in the neural network are converted to explicit weights. Experiments show that the method is more accurate than art-of-state methods. Furthermore, the method can be used in more fields, where the differentiable neural network can be used. The types of data can be discrete, continuous, or labeled, and the number of output data items is unlimited

How Good Can a Face Identifier Be Without Learning

Constructing discriminative features is an essential issue in developing face recognition algorithms. There are two schools in how features are constructed: hand-crafted features and learned features from data. A clear trend in the face recognition community is to use learned features to replace hand-crafted ones for face recognition, due to the superb performance achieved by learned features through Deep Learning networks. Given the negative aspects of database-dependent solutions, we consider an alternative and demonstrate that, for good generalization performance, developing face recognition algorithms by using handcrafted features is surprisingly promising when the training dataset is small or medium sized. We show how to build such a face identifier with our Block Matching method which leverages the power of the Gabor phase in face images. Although no learning process is involved, empirical results show that the performance of this “designed” identifier is comparable (superior) to state-of-the-art identifiers and even close to Deep Learning approaches.QC 20170208</p