计算机科学
相似性(几何)
域适应
模式识别(心理学)
领域(数学分析)
卷积神经网络
人工智能
箱子
相似性度量
学习迁移
数据挖掘
特征(语言学)
算法
图像(数学)
数学
哲学
数学分析
分类器(UML)
语言学
作者
Tianyu Han,Lifeng Zhang,Shixiang Jia
摘要
Fine-grained classification tasks are challenging because fine-grained data sets are quite scarce. Thus, we utilized the domain adaptation method to migrate knowledge from large, labeled data sets to fine-grained target data sets. We employed the bin similarity (BS) algorithm to measure and select the approximate domains from large-scale data sets to the fine-grained target domains. Source domain feature space was divided into multiple bins and the features of the target domains were sampled to fill the bins. The most similar domains were selected based on the similarity statistics of the sample features. We implemented the BS algorithm combined with the popular convolutional neural networks, pretrained the network on the selected similar subdata sets, and subsequently fine-tuned it on the fine-grained data sets. We evaluated the BS classification model on Stanford Dogs and Oxford Flower data sets, and the results showed improved BS classification performance compared with the state-of-the-art domain adaptation methods, earth mover's distance, selective joint fine-tuning, L2 with starting point, and domain similarity for transfer learning. Furthermore, BS is a pluggable module that boosts the performance of domain adaptation.
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