In this paper, we address the problem of adapting models from a source domain
to a target domain, a task that has become increasingly important due to the
brittle generalization of deep neural networks. While several test-time
adaptation techniques have emerged, they typically rely on synthetic toolbox
data augmentations in cases of limited target data availability. We consider
the challenging setting of single-shot adaptation and explore the design of
augmentation strategies. We argue that augmentations utilized by existing
methods are insufficient to handle large distribution shifts, and hence propose
a new approach SiSTA, which first fine-tunes a generative model from the source
domain using a single-shot target, and then employs novel sampling strategies
for curating synthetic target data. Using experiments on a variety of
benchmarks, distribution shifts and image corruptions, we find that SiSTA
produces significantly improved generalization over existing baselines in face
attribute detection and multi-class object recognition. Furthermore, SiSTA
performs competitively to models obtained by training on larger target
datasets. Our codes can be accessed at this https URL

本文提出了一种新颖的域适应方法 SiSTA，该方法利用单次训练目标任务样本 fine-tuned 生成模型，以产生配对的真实目标标注数据，并证明在面部属性检测和多类对象识别等基准测试中，SiSTA 相对其他方法表现更优秀。

单次适应的目标感知生成增强

Target-Aware Generative Augmentations for Single-Shot Adaptation

In this work, we provide a recipe for training machine translation models in
a limited resource setting by leveraging synthetic target data generated using
a large pre-trained model. We show that consistently across different
benchmarks in bilingual, multilingual, and speech translation setups, training
models on synthetic targets outperforms training on the actual ground-truth
data. This performance gap grows bigger with increasing limits on the amount of
available resources in the form of the size of the dataset and the number of
parameters in the model. We also provide preliminary analysis into whether this
boost in performance is linked to ease of optimization or more deterministic
nature of the predictions, and whether this paradigm leads to better
out-of-distribution performance across different testing domains.

使用大型预训练模型生成合成目标数据可以提高限定资源环境下机器翻译模型的性能，这种方法可以优于在实际数据上训练模型，效果更好的原因可以归因于模型参数的数量限制和优化效果的提高。