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Author Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer
Title Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation Type Conference Article
Year 2022 Publication 36th Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We propose a simple but effective source-free domain adaptation (SFDA) method.
Treating SFDA as an unsupervised clustering problem and following the intuition
that local neighbors in feature space should have more similar predictions than
other features, we propose to optimize an objective of prediction consistency. This
objective encourages local neighborhood features in feature space to have similar
predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method.
Address Virtual; November 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference NEURIPS
Notes LAMP; 600.147 Approved no
Call Number Admin @ si @ YWW2022a Serial 3792
Permanent link to this record
 
 
Author Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer
Title Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. This objective encourages local neighborhood features in feature space to have similar predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. Code is available in this https URL.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes LAMP; 600.147 Approved no
Call Number Admin @ si @ YWW2022b Serial 3815
Permanent link to this record
 
 
Author Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer
Title One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this paper, we investigate model adaptation under domain and category shift, where the final goal is to achieve
(SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. We propose a novel training scheme to learn a (
+1)-way classifier to predict the
source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show:
After source training, the resulting source model can get excellent performance for
;
After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method.
When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes LAMP; no proj Approved no
Call Number Admin @ si @ YWW2022c Serial 3818
Permanent link to this record