Active Domain Adaptation Based on Neighbor Environment Perception Sample Selection

Chen Xin-yu; Zhu Jian; Chen Bing-feng; Cai Rui-chu

doi:10.12052/gdutxb.230172

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Active Domain Adaptation Based on Neighbor Environment Perception Sample Selection

更新时间：2026-05-27

- Active Domain Adaptation Based on Neighbor Environment Perception Sample Selection
- Vol. 41, Issue 6, Pages: 80-90(2024)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230172
  CLC： TP181
- Published：2024
- 稿件说明：
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陈鑫瑀, 朱鉴, 陈炳丰, 蔡瑞初. 一种基于邻居环境感知的主动领域自适应算法[J]. 广东工业大学学报, 2024, 41(6): 80-90.

Chen Xin-yu, Zhu Jian, Chen Bing-feng, et al. Active Domain Adaptation Based on Neighbor Environment Perception Sample Selection[J]. 2024, 41(6): 80-90.
陈鑫瑀, 朱鉴, 陈炳丰, 蔡瑞初. 一种基于邻居环境感知的主动领域自适应算法[J]. 广东工业大学学报, 2024, 41(6): 80-90. DOI： 10.12052/gdutxb.230172.

Chen Xin-yu, Zhu Jian, Chen Bing-feng, et al. Active Domain Adaptation Based on Neighbor Environment Perception Sample Selection[J]. 2024, 41(6): 80-90. DOI： 10.12052/gdutxb.230172.

摘要

主动领域自适应 (Active Domain Adaptation

ADA) 目的是在领域自适应的背景下利用尽可能少的目标域查询预算来训练一个有效模型。然而，由于领域漂移的存在，现有的算法选择的实例可能是信息量低、冗余或离群的。为了解决这个问题，本文提出了一种新的方法用于主动领域自适应，即基于邻居环境感知的样本选择算法 (Neighbor Environment Perception Sample Selection

NEPS)。NEPS以一种邻居环境感知的方式来探索目标样本的信息量，以选择在领域转移下可能最有价值的实例。具体而言，在计算样本信息量时，测量所提出的邻居环境感知信息得分 (Neighbor Awareness Informativeness Score

NAIS) 的同时利用从单个数据点以及从其附近的邻居所获得的信息，从而确保所选样本具有较高的单点及环境信息量。同时，通过计算候选样本与已标记样本的相似度分数来对样本进行排序挑选，以保证所选样本的多样性。此外，还充分利用了所有已标记样本以及目标领域的大量未标记数据信息，进而提高模型的性能。经验证，本文方法具有较强的样本选择能力，在各种基准数据集上的分类效果都优于现有的模型。

Abstract

Active domain adaptation (ADA) aims to train an effective model under the context of domain adaptation with as few queried instances as possible. However

existing algorithms tend to select instances that are either uninformative

redundant

or outliers due to domain shift. To address this issue

a novel approach called neighbor environment perception sample selection (NEPS) for active domain adaptation is proposed. NEPS explores the target sample informativeness in a neighbor environment-aware manner to select instances that are potentially most valuable under domain shift. Specifically

from informativeness perspective

NEPS aims to acquire knowledge not only from individual data points but also from their neighboring samples. This is achieved by measuring neighbor awareness informativeness score (NAIS)

which ensures the selected samples have both high individual informativeness score and environment informativeness score. Additionally

NEPS ranks and selects samples based on their similarity scores with labeled samples to ensure diversity among the chosen instances. Furthermore

NEPS makes effective use of all labeled samples as well as a large amount of unlabeled data from the target domain to enhance the model's performance. Experimental results demonstrate that NEPS exhibits strong sample selection capability and outperforms existing models in terms of classification performance on various benchmark datasets.

关键词

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references

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