semi-supervised Hash learning method and device
A learning method and a learning device technology, which are applied in the field of semi-supervised hash learning methods and devices, and can solve problems such as inadvisability, unsatisfactory deep hash learning effect, and high human and financial resource consumption.
Active Publication Date: 2019-05-17
TSINGHUA UNIV
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Problems solved by technology
However, in practical applications, the scale of data is huge (such as hundreds of millions of pictures), and manual labeling of these data for training requires a lot of human and financial resources, which is almost impossible; secondly, data labeling may be the relationship between data pairs, such as social The friend relationship of the network, the relationship defined by the image text description, etc., make it not advisable to directly label the data category
The above problems all lead to the unsatisfactory effect of deep hash learning
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[0107] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numbers in the figures indicate functionally identical or similar elements. While various aspects of the embodiments are shown in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
[0108] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as superior or better than other embodiments.
[0109] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific implementation manners. It will be understood by those skilled in the art that the present disclosure may be practiced without some of the specific details. In some instances, methods, means, componen...
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The invention relates to a semi-supervised Hash learning method and device, and the method comprises the steps: inputting sample data into a teacher function for processing, obtaining a teacher function output Hash code, inputting the sample data into a student function for processing, and obtaining a student function output Hash code; determining loss of the student function according to the sample data, the teacher function output hash code and the student function output hash code; reversely propagating the loss gradient to the student function so as to adjust parameters of the student function and complete one-time training of the student function; And determining the adjusted parameters of the teacher function according to the adjusted parameters of the student function and the parameters of the teacher function to be adjusted, and completing the primary training of the teacher function. According to the embodiment of the invention, a small number of labeled sample data and a large number of unlabeled sample data can be used for training. The teacher function and the student function which are obtained by carrying out joint training on the teacher function and the student function can be used for efficiently and accurately obtaining Hash codes of data so as to be used for more efficient retrieval.
Description
technical field [0001] The present disclosure relates to the technical field of neural networks, in particular to a semi-supervised hash learning method and device. Background technique [0002] With the development of artificial intelligence and information retrieval technology, there are more and more retrieval requirements for complex data such as images. Taking image retrieval as an example, given an image, we want to find images that are similar at the pixel or semantic level. Due to the complex structure and high dimensionality of images, the efficiency and accuracy of image retrieval has become a difficulty for large-scale image data. [0003] The deep hash learning method is a traditional solution to the retrieval of large-scale complex data (such as images, etc.). Hash learning can learn a specific hash function, and map high-dimensional complex data to short binary hash codes, so that the Hamming distance of hash codes for similar data (such as similar image pixe...
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Inventor 张世枫李建民张钹
Owner TSINGHUA UNIV