Method for improving model domain adaptivity based on Markov open composite domain
A self-adaptive, composite domain technology, applied in the field of computer vision, can solve the problems of sufficient, inability to guarantee the distribution of individuals in the domain, loss of data information, etc., and achieve the effect of sufficient extraction and good domain adaptation effect.
Active Publication Date: 2021-05-25
BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
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[0007] In composite domains, there is no guarantee that the distribution among individuals in the domain is sufficient;
[0008] The constructed encoder sets all the data information less than 0 to 0, so that the data information less than 0 is lost
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[0064] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.
[0065] Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of said features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components, and / or groups thereof. It will be understoo...
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According to the method for improving the model domain adaptability based on the Markov open composite domain; the concept of the Markov open composite domain is provided, different data sets are mixed together through the Markov process, it is ensured that elements from the different data sets in the composite domain are distributed more dispersedly, and therefore, a better domain adaptation effect is achieved. The method provided by the invention also has the following beneficial effects: a plurality of domains without domain labels are mixed into a Markov composite domain by applying a Markov process and combining a Bernoulli large number theorem, and then the Markov composite domain is combined with an open domain to form a Markov open composite domain, so that elements from different data sets in the composite domain are distributed more dispersedly, and therefore, a better domain self-adaption effect is achieved; meanwhile, a neural network encoder based on a parameterized correction linear unit is constructed, so that the neural network can fully utilize all data information in the encoding process, and the purpose of fully extracting image features is achieved.
Description
technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for improving model domain adaptability based on Markov open composite domains. Background technique [0002] Image recognition is a technology that uses computers to process, analyze and understand images, and to identify targets and objects in different patterns. It has a very important influence and role in the process of intelligent data collection and processing with images as the main body. There is no essential difference between computer image recognition technology and human image recognition in principle, but the machine lacks the influence of humans on sensory and visual differences. When human beings see a picture, our brain will quickly sense whether we have seen this picture or a picture similar to it, and our brain will identify it according to the categories that have been classified in the stored memory to see if there is something similar to the...
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IPC IPC(8): G06K9/62G06K9/46
CPCG06V10/457G06F18/2415G06F18/214
Inventor 谭志刘兴业
Owner BEIJING UNIV OF CIVIL ENG & ARCHITECTURE