Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2411.15844v1
• Date: Sun, 24 Nov 2024 13:49:29 GMT
• Title: Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation
• Authors: Fan Wang, Zhongyi Han, Xingbo Liu, Xin Gao, Yilong Yin,
• Abstract summary: We show that Source-Free Domain Adaptation (SFDA) generally outperforms Unsupervised Domain Adaptation (UDA) in real-world scenarios. SFDA offers advantages in time efficiency, storage requirements, targeted learning objectives, reduced risk of negative transfer, and increased robustness against overfitting. We propose a novel weight estimation method that effectively integrates available source data into multi-SFDA approaches.
• Score: 52.36436121884317
• License:
• Abstract: In domain adaptation, there are two popular paradigms: Unsupervised Domain Adaptation (UDA), which aligns distributions using source data, and Source-Free Domain Adaptation (SFDA), which leverages pre-trained source models without accessing source data. Evaluating the superiority of UDA versus SFDA is an open and timely question with significant implications for deploying adaptive algorithms in practical applications. In this study, we demonstrate through predictive coding theory and extensive experiments on multiple benchmark datasets that SFDA generally outperforms UDA in real-world scenarios. Specifically, SFDA offers advantages in time efficiency, storage requirements, targeted learning objectives, reduced risk of negative transfer, and increased robustness against overfitting. Notably, SFDA is particularly effective in mitigating negative transfer when there are substantial distribution discrepancies between source and target domains. Additionally, we introduce a novel data-model fusion scenario, where data sharing among stakeholders varies (e.g., some provide raw data while others provide only models), and reveal that traditional UDA and SFDA methods do not fully exploit their potential in this context. To address this limitation and capitalize on the strengths of SFDA, we propose a novel weight estimation method that effectively integrates available source data into multi-SFDA (MSFDA) approaches, thereby enhancing model performance within this scenario. This work provides a thorough analysis of UDA versus SFDA and advances a practical approach to model adaptation across diverse real-world environments.

Related papers

• Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation [8.791916654073088]
  Source-free domain adaptation (SFDA) alleviates the domain discrepancy among data obtained from domains without accessing the data for the awareness of data privacy. We propose Uncertainty-aware Adaptive Distillation (UAD) for the multi-source-free unsupervised domain adaptation setting.
  arXiv  Detail & Related papers  (2024-02-09T06:48:04Z)
• Open-Set Domain Adaptation with Visual-Language Foundation Models [51.49854335102149]
  Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge from a source domain to a target domain with unlabeled data. Open-set domain adaptation (ODA) has emerged as a potential solution to identify these classes during the training phase.
  arXiv  Detail & Related papers  (2023-07-30T11:38:46Z)
• Domain Alignment Meets Fully Test-Time Adaptation [24.546705919244936]
  A foundational requirement of a deployed ML model is to generalize to data drawn from a testing distribution that is different from training. In this paper, we focus on a challenging variant of this problem, where access to the original source data is restricted. We propose a new approach, CATTAn, that bridges UDA and FTTA, by relaxing the need to access entire source data.
  arXiv  Detail & Related papers  (2022-07-09T03:17:19Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• UMAD: Universal Model Adaptation under Domain and Category Shift [138.12678159620248]
  Universal Model ADaptation (UMAD) framework handles both UDA scenarios without access to source data. We develop an informative consistency score to help distinguish unknown samples from known samples. Experiments on open-set and open-partial-set UDA scenarios demonstrate that UMAD exhibits comparable, if not superior, performance to state-of-the-art data-dependent methods.
  arXiv  Detail & Related papers  (2021-12-16T01:22:59Z)
• Robustified Domain Adaptation [13.14535125302501]
  Unsupervised domain adaptation (UDA) is widely used to transfer knowledge from a labeled source domain to an unlabeled target domain. The inevitable domain distribution deviation in UDA is a critical barrier to model robustness on the target domain. We propose a novel Class-consistent Unsupervised Domain Adaptation (CURDA) framework for training robust UDA models.
  arXiv  Detail & Related papers  (2020-11-18T22:21:54Z)
• Towards Inheritable Models for Open-Set Domain Adaptation [56.930641754944915]
  We introduce a practical Domain Adaptation paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future. We present an objective way to quantify inheritability to enable the selection of the most suitable source model for a given target domain, even in the absence of the source data.
  arXiv  Detail & Related papers  (2020-04-09T07:16:30Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.