Small sample learning method and system based on absolute-relative learning architecture
The invention provides a small sample learning method and system based on an absolute-relative learning architecture, and the method comprises the steps: calling a representation extraction module, and carrying out representation extraction of each image sample in a training set, so as to obtain a f...
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Main Authors | , , , , |
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Format | Patent |
Language | Chinese English |
Published |
13.08.2021
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Abstract | The invention provides a small sample learning method and system based on an absolute-relative learning architecture, and the method comprises the steps: calling a representation extraction module, and carrying out representation extraction of each image sample in a training set, so as to obtain a feature vector of each image sample; calling an absolute learning module to train the feature vector of each image sample to determine a category-based first prediction result and a semantic-based second prediction result of each image sample; combining the feature vectors of every two image samples into a group of sample feature pairs, and splicing the two feature vectors in each group of sample feature pairs into a group to form a vector; calling a relative learning module to train the sample feature pairs so as to determine a category-based first similarity and a semantic-based second similarity of two feature vectors in each group of sample feature pairs; and calculating a loss function of the model according to |
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AbstractList | The invention provides a small sample learning method and system based on an absolute-relative learning architecture, and the method comprises the steps: calling a representation extraction module, and carrying out representation extraction of each image sample in a training set, so as to obtain a feature vector of each image sample; calling an absolute learning module to train the feature vector of each image sample to determine a category-based first prediction result and a semantic-based second prediction result of each image sample; combining the feature vectors of every two image samples into a group of sample feature pairs, and splicing the two feature vectors in each group of sample feature pairs into a group to form a vector; calling a relative learning module to train the sample feature pairs so as to determine a category-based first similarity and a semantic-based second similarity of two feature vectors in each group of sample feature pairs; and calculating a loss function of the model according to |
Author | MA LINRU LI DONGYANG BAO JINZHEN ZHANG HONGGUANG YANG XIONGJUN |
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DocumentTitleAlternate | 一种基于绝对-相对学习架构的小样本学习方法和系统 |
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Snippet | The invention provides a small sample learning method and system based on an absolute-relative learning architecture, and the method comprises the steps:... |
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SubjectTerms | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
Title | Small sample learning method and system based on absolute-relative learning architecture |
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