Contrastive pretraining is well-known to improve downstream task performance
and model generalisation, especially in limited label settings. However, it is
sensitive to the choice of augmentation pipeline. Positive pairs should
preserve semantic information while destroying domain-specific information.
Standard augmentation pipelines emulate domain-specific changes with
pre-defined photometric transformations, but what if we could simulate
realistic domain changes instead? In this work, we show how to utilise recent
progress in counterfactual image generation to this effect. We propose
CF-SimCLR, a counterfactual contrastive learning approach which leverages
approximate counterfactual inference for positive pair creation. Comprehensive
evaluation across five datasets, on chest radiography and mammography,
demonstrates that CF-SimCLR substantially improves robustness to acquisition
shift with higher downstream performance on both in- and out-of-distribution
data, particularly for domains which are under-represented during training.

利用计数事实图像生成的最新进展，我们提出了 CF-SimCLR，一种反事实对比学习方法，利用近似的反事实推理用于正样本对的创建，在胸部放射学和乳房 X 线检查的五个数据集上进行综合评估，证明 CF-SimCLR 可以显著提高对获得偏移的稳健性，在内部和外部分布的数据上都能获得更高的下游性能，特别是对于在训练过程中少见的领域。

对事实对比学习的鲁棒表达：通过因果图像合成

Counterfactual contrastive learning: robust representations via causal  image synthesis

With the increasing availability of large databases of 3D CAD models,
depth-based recognition methods can be trained on an uncountable number of
synthetically rendered images. However, discrepancies with the real data
acquired from various depth sensors still noticeably impede progress. Previous
works adopted unsupervised approaches to generate more realistic depth data,
but they all require real scans for training, even if unlabeled. This still
represents a strong requirement, especially when considering
real-life/industrial settings where real training images are hard or impossible
to acquire, but texture-less 3D models are available. We thus propose a novel
approach leveraging only CAD models to bridge the realism gap. Purely trained
on synthetic data, playing against an extensive augmentation pipeline in an
unsupervised manner, our generative adversarial network learns to effectively
segment depth images and recover the clean synthetic-looking depth information
even from partial occlusions. As our solution is not only fully decoupled from
the real domains but also from the task-specific analytics, the pre-processed
scans can be handed to any kind and number of recognition methods also trained
on synthetic data. Through various experiments, we demonstrate how this
simplifies their training and consistently enhances their performance, with
results on par with the same methods trained on real data, and better than
usual approaches doing the reverse mapping.

本研究提出了一种基于 CAD 模型、使用生成式对抗网络的增强方法进行无监督训练，从而更好地识别深度图像并恢复可靠的深度信息。该方法不但简化了识别算法的训练，通过各种实验，还证明了其性能优于传统方法。