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Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation
Shiqi Yang · yaxing wang · kai wang · Shangling Jui · Joost van de Weijer

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #127

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 https://github.com/Albert0147/AaD_SFDA.

Author Information

Shiqi Yang (Computer Vision Center Barcelona)

Edifici O campus UAB ESQ5856375J

yaxing wang (Centre de Visió per Computador (CVC))
kai wang (Computer Vision Center, UAB)
Shangling Jui (Huawei)

Dr. Jui is the chief AI scientist of Huawei Kirin team. His knowledge on AI and reinforcement learning has guided the team to build the eco-system of Kirin platform. He support decisions and investment of AI to Canadian universities including UBC, SFU, UofToronto, UofAlberta, UofWaterloo, etc., through joint lab collaborations and local Huawei offices.

Joost van de Weijer (Computer Vision Center Barcelona)

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