Paper Info
Title
Confidence Regularized Label Propagation Based Domain Adaptation
Abstract
In domain adaptation (DA), label-induced losses generally occupy a dominant position and most previous models regard hard or soft labels as their inputs. However, these two types of labels may mislead the modeling process of label-induced losses since hard label is sensitive to a wrongly-predicted sample while soft label may introduce label noise, thus they may cause negative transfer. To relieve this problem, we propose a novel label learning approach namely confidence regularized label propagation (CRLP) that regularizes the confidence of predicted soft labels with constraints of F-norm or L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> -norm. It is validated that maximizing either one of these two constraints equals to minimizing entropy loss. Specially, we illustrate that L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> -norm is more suitable for DA than F-norm when the dataset contain a large number of categories. Then, we leverage the regularized soft labels produced by CRLP to reformulate some popular label-induced losses that consider feature transferability and discriminability such as class-wise maximum mean discrepancy, intra-class compactness and inter-class dispersion in a probability manner to present a novel DA method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , CRLP-DA). Comprehensive analysis and experiments on four cross-domain object recognition datasets verify that the proposed CRLP-DA outperforms some state-of-the-art methods, especially 59.5% for Office10+Caltech10 dataset with SURF features. For others to better reproduce, our preliminary Matlab code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WWLoveTransfer/CRLP-DA/</uri> .
Year
DOI
Venue
2022
10.1109/TCSVT.2021.3104835
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
DocType
Volume
Domain adaptation,hard label,soft label,label propagation,entropy loss,F/L₂₁-norm,label-induced losses
Journal
32
Issue
ISSN
Citations 
6
1051-8215
0
PageRank 
References 
Authors
0.34
33
6
Name
Order
Citations
PageRank
Wei Wang100.34
Baopu Li234830.88
Mengzhu Wang323.44
Feiping Nie47061309.42
zhihui wang53912.86
Haojie Li6142765.70