Synthetic training data has gained prominence in numerous learning tasks and
scenarios, offering advantages such as dataset augmentation, generalization
evaluation, and privacy preservation. Despite these benefits, the efficiency of
synthetic data generated by current methodologies remains inferior when
training advanced deep models exclusively, limiting its practical utility. To
address this challenge, we analyze the principles underlying training data
synthesis for supervised learning and elucidate a principled theoretical
framework from the distribution-matching perspective that explicates the
mechanisms governing synthesis efficacy. Through extensive experiments, we
demonstrate the effectiveness of our synthetic data across diverse image
classification tasks, both as a replacement for and augmentation to real
datasets, while also benefits challenging tasks such as out-of-distribution
generalization and privacy preservation.

我们通过广泛的实验验证了我们的合成数据在各种图像分类任务中的有效性，既可以替代真实数据集，也可以作为其增强，同时也有益于挑战性任务，如超出分布的泛化和隐私保护。

真假：通过分布匹配进行有效的训练数据合成

Real-Fake: Effective Training Data Synthesis Through Distribution  Matching

Deep Learning failure cases are abundant, particularly in the medical area.
Recent studies in out-of-distribution generalization have advanced considerably
on well-controlled synthetic datasets, but they do not represent medical
imaging contexts. We propose a pipeline that relies on artifacts annotation to
enable generalization evaluation and debiasing for the challenging skin lesion
analysis context. First, we partition the data into levels of increasingly
higher biased training and test sets for better generalization assessment.
Then, we create environments based on skin lesion artifacts to enable domain
generalization methods. Finally, after robust training, we perform a test-time
debiasing procedure, reducing spurious features in inference images. Our
experiments show our pipeline improves performance metrics in biased cases, and
avoids artifacts when using explanation methods. Still, when evaluating such
models in out-of-distribution data, they did not prefer clinically-meaningful
features. Instead, performance only improved in test sets that present similar
artifacts from training, suggesting models learned to ignore the known set of
artifacts. Our results raise a concern that debiasing models towards a single
aspect may not be enough for fair skin lesion analysis.

提出了一种基于标注伪影的流程，用于深度学习在皮肤病变分析中的领域适应性评估与去偏倚。在具有不同程度偏差的数据集上进行建模，并利用基于皮肤病变的数据集进行域通用化。在测试时去除推理图像中的伪特征，实验表明该流程在有偏数据集的表现指标有所提高，且对于伪影的忽略具有较好的效果，但在外部数据集上的表现较差，尤其需要对于皮肤病变分析的公平性更加关注。

基于物品的皮肤病变模型领域泛化

Artifact-Based Domain Generalization of Skin Lesion Models

Table Question Answering (TQA) is an important but under-explored task. Most
of the existing QA datasets are in unstructured text format and only few of
them use tables as the context. To the best of our knowledge, none of TQA
datasets exist in the biomedical domain where tables are frequently used to
present information. In this paper, we first curate a table question answering
dataset, BioTABQA, using 22 templates and the context from a biomedical
textbook on differential diagnosis. BioTABQA can not only be used to teach a
model how to answer questions from tables but also evaluate how a model
generalizes to unseen questions, an important scenario for biomedical
applications. To achieve the generalization evaluation, we divide the templates
into 17 training and 5 cross-task evaluations. Then, we develop two baselines
using single and multi-tasks learning on BioTABQA. Furthermore, we explore
instructional learning, a recent technique showing impressive generalizing
performance. Experimental results show that our instruction-tuned model
outperforms single and multi-task baselines on an average by ~23% and ~6%
across various evaluation settings, and more importantly, instruction-tuned
model outperforms baselines by ~5% on cross-tasks.

本文在生物医学领域维度构建了一个表格问答数据集 BioTABQA，包含 22 个模板和上下文中的信息。通过该数据集，本文提出了一种基于指导学习的方法，在多个评估方式下，该方法比单一和多任务基线模型表现提高约 23% 和 6%。最重要的是，在跨任务方面，该方法的性能比基线模型提高了约 5%。