Source-free Unsupervised Domain Adaptation (SF-UDA) aims to adapt a
well-trained source model to an unlabeled target domain without access to the
source data. One key challenge is the lack of source data during domain
adaptation. To handle this, we propose to mine the hidden knowledge of the
source model and exploit it to generate source avatar prototypes. To this end,
we propose a Contrastive Prototype Generation and Adaptation (CPGA) method.
CPGA consists of two stages: Prototype generation and Prototype adaptation.
Extensive experiments on three UDA benchmark datasets demonstrate the
superiority of CPGA. However, existing SF.UDA studies implicitly assume
balanced class distributions for both the source and target domains, which
hinders their real applications. To address this issue, we study a more
practical SF-UDA task, termed imbalance-agnostic SF-UDA, where the class
distributions of both the unseen source domain and unlabeled target domain are
unknown and could be arbitrarily skewed. This task is much more challenging
than vanilla SF-UDA due to the co-occurrence of covariate shifts and
unidentified class distribution shifts between the source and target domains.
To address this task, we extend CPGA and propose a new Target-aware Contrastive
Prototype Generation and Adaptation (T-CPGA) method. Specifically, for better
prototype adaptation in the imbalance-agnostic scenario, T-CPGA applies a new
pseudo label generation strategy to identify unknown target class distribution
and generate accurate pseudo labels, by utilizing the collective intelligence
of the source model and an additional contrastive language-image pre-trained
model. Meanwhile, we further devise a target label-distribution-aware
classifier to adapt the model to the unknown target class distribution. We
empirically show that T-CPGA significantly outperforms CPGA and other SF-UDA
methods in imbalance-agnostic SF-UDA.

该论文提出了一个基于原模型中的隐藏知识，利用其生成原型并进行适应的对比原型生成和适应方法，以在没有源数据的情况下适应未标记的目标域，同时解决了存在不平衡类分布的实用情况，并通过新的伪标签生成策略和针对性的分类器，优化模型适应度，有望在应用中取得更好表现。

基于 Avatar 原型对齐的不考虑不平衡源域自适应

Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype  Alignment

We study a practical domain adaptation task, called source-free unsupervised
domain adaptation (UDA) problem, in which we cannot access source domain data
due to data privacy issues but only a pre-trained source model and unlabeled
target data are available. This task, however, is very difficult due to one key
challenge: the lack of source data and target domain labels makes model
adaptation very challenging. To address this, we propose to mine the hidden
knowledge in the source model and exploit it to generate source avatar
prototypes (i.e., representative features for each source class) as well as
target pseudo labels for domain alignment. To this end, we propose a
Contrastive Prototype Generation and Adaptation (CPGA) method. Specifically,
CPGA consists of two stages: (1) prototype generation: by exploring the
classification boundary information of the source model, we train a prototype
generator to generate avatar prototypes via contrastive learning. (2) prototype
adaptation: based on the generated source prototypes and target pseudo labels,
we develop a new robust contrastive prototype adaptation strategy to align each
pseudo-labeled target data to the corresponding source prototypes. Extensive
experiments on three UDA benchmark datasets demonstrate the effectiveness and
superiority of the proposed method.

本文提出了一种新的方法，称为反映原型的对比生成和调整方法（CPGA），用于解决源自由的无监督域适应问题，并证明其在三个 UDA 基准数据集上的有效性和优越性。