Real-world deployment of computer vision systems, including in the discovery
processes of biomedical research, requires causal representations that are
invariant to contextual nuisances and generalize to new data. Leveraging the
internal replicate structure of two novel single-cell fluorescent microscopy
datasets, we propose generally applicable tests to assess the extent to which
models learn causal representations across increasingly challenging levels of
OOD-generalization. We show that despite seemingly strong performance, as
assessed by other established metrics, both naive and contemporary baselines
designed to ward against confounding, collapse on these tests. We introduce a
new method, Interventional Style Transfer (IST), that substantially improves
OOD generalization by generating interventional training distributions in which
spurious correlations between biological causes and nuisances are mitigated. We
publish our code and datasets.

本文提出了一种名为 Interventional Style Transfer 的方法，是针对在计算机视觉系统的实际应用过程中，包括在生物医学研究的发现过程中，需要具有一种对上下文干扰具有不变性及对新数据具有普遍适用性的因果表示的问题，在两个新的单细胞荧光显微镜数据集的内部复制结构中进行了验证，证明了该方法可以显著地提高 OOD 的泛化能力。

单细胞显微镜中基于干预式转移的分布外泛化

Out of Distribution Generalization via Interventional Style Transfer in  Single-Cell Microscopy

Foundation models are subject to an ongoing heated debate, leaving open the
question of progress towards AGI and dividing the community into two camps: the
ones who see the arguably impressive results as evidence to the scaling
hypothesis, and the others who are worried about the lack of interpretability
and reasoning capabilities. By investigating to which extent causal
representations might be captured by these large scale language models, we make
a humble efforts towards resolving the ongoing philosophical conflicts.

研究基础模型对因果关系表示的能力，并探讨其与 AGI 进展之间的关系及其可解释性和推理能力，以帮助解决该领域的哲学争议。

基础模型是否能够探讨因果关系？

Can Foundation Models Talk Causality?

Understanding the latent causal factors of a dynamical system from visual
observations is considered a crucial step towards agents reasoning in complex
environments. In this paper, we propose CITRIS, a variational autoencoder
framework that learns causal representations from temporal sequences of images
in which underlying causal factors have possibly been intervened upon. In
contrast to the recent literature, CITRIS exploits temporality and observing
intervention targets to identify scalar and multidimensional causal factors,
such as 3D rotation angles. Furthermore, by introducing a normalizing flow,
CITRIS can be easily extended to leverage and disentangle representations
obtained by already pretrained autoencoders. Extending previous results on
scalar causal factors, we prove identifiability in a more general setting, in
which only some components of a causal factor are affected by interventions. In
experiments on 3D rendered image sequences, CITRIS outperforms previous methods
on recovering the underlying causal variables. Moreover, using pretrained
autoencoders, CITRIS can even generalize to unseen instantiations of causal
factors, opening future research areas in sim-to-real generalization for causal
representation learning.

本文提出了 CITRIS，一个基于变分自编码器的框架，通过观察可能受到干预的图像序列，学习掌握因果关系。与过去文献不同的是，CITRIS 结合了时间因素和观察干预目标，实现了识别标量和多维因果因素（例如 3D 旋转角度）的能力，并使用归一化流扩展到更广泛的情景。实验结果表明，CITRIS 优于以前的方法，甚至可以泛化到因果变量的新实例。