We propose a learning problem involving adapting a pre-trained source model
to the target domain for classifying all classes that appeared in the source
data, using target data that covers only a partial label space. This problem is
practical, as it is unrealistic for the target end-users to collect data for
all classes prior to adaptation. However, it has received limited attention in
the literature. To shed light on this issue, we construct benchmark datasets
and conduct extensive experiments to uncover the inherent challenges. We found
a dilemma -- on the one hand, adapting to the new target domain is important to
claim better performance; on the other hand, we observe that preserving the
classification accuracy of classes missing in the target adaptation data is
highly challenging, let alone improving them. To tackle this, we identify two
key directions: 1) disentangling domain gradients from classification
gradients, and 2) preserving class relationships. We present several effective
solutions that maintain the accuracy of the missing classes and enhance the
overall performance, establishing solid baselines for holistic transfer of
pre-trained models with partial target data.

我们提出了一个学习问题，涉及将预先训练的源模型适应到目标领域，用目标数据对仅覆盖部分标签空间的所有类进行分类。我们发现在目标适应数据中保持缺失类的准确性以及提高总体性能是具有挑战性的，并提出了解决方法，包括分离领域梯度和分类梯度，以及保留类之间的关系。通过这些有效的解决方案，我们建立了预训练模型在部分目标数据上的整体转移的坚实基线。

整体式迁移：以部分目标数据进行非干扰微调

Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial  Target Data

Source-free domain adaptation (SFDA) aims to adapt a classifier to an
unlabelled target data set by only using a pre-trained source model. However,
the absence of the source data and the domain shift makes the predictions on
the target data unreliable. We propose quantifying the uncertainty in the
source model predictions and utilizing it to guide the target adaptation. For
this, we construct a probabilistic source model by incorporating priors on the
network parameters inducing a distribution over the model predictions.
Uncertainties are estimated by employing a Laplace approximation and
incorporated to identify target data points that do not lie in the source
manifold and to down-weight them when maximizing the mutual information on the
target data. Unlike recent works, our probabilistic treatment is
computationally lightweight, decouples source training and target adaptation,
and requires no specialized source training or changes of the model
architecture. We show the advantages of uncertainty-guided SFDA over
traditional SFDA in the closed-set and open-set settings and provide empirical
evidence that our approach is more robust to strong domain shifts even without
tuning.

该研究提出了一种基于概率模型的源自由领域自适应方法，通过量化源模型预测中的不确定性来指导目标适应，以对抗域漂移和缺乏源数据的问题，并且相比于传统的源自由领域自适应方法，该方法计算轻量、与源训练和目标适应相独立。