Counterfactual image editing is an important task in generative AI, which
asks how an image would look if certain features were different. The current
literature on the topic focuses primarily on changing individual features while
remaining silent about the causal relationships between these features, as
present in the real world. In this paper, we formalize the counterfactual image
editing task using formal language, modeling the causal relationships between
latent generative factors and images through a special type of model called
augmented structural causal models (ASCMs). Second, we show two fundamental
impossibility results: (1) counterfactual editing is impossible from i.i.d.
image samples and their corresponding labels alone; (2) even when the causal
relationships between the latent generative factors and images are available,
no guarantees regarding the output of the model can be provided. Third, we
propose a relaxation for this challenging problem by approximating
non-identifiable counterfactual distributions with a new family of
counterfactual-consistent estimators. This family exhibits the desirable
property of preserving features that the user cares about across both factual
and counterfactual worlds. Finally, we develop an efficient algorithm to
generate counterfactual images by leveraging neural causal models.

反事实图像编辑是生成式人工智能中的重要任务，本文提出了一种利用增强的结构因果模型来建模潜在生成因子与图像之间的因果关系，并通过一种新的家族反事实一致估计器对非可识别的反事实分布进行近似，以解决此挑战性问题。

反事实图像编辑

Counterfactual Image Editing

State representation learning, or the ability to capture latent generative
factors of an environment, is crucial for building intelligent agents that can
perform a wide variety of tasks. Learning such representations without
supervision from rewards is a challenging open problem. We introduce a method
that learns state representations by maximizing mutual information across
spatially and temporally distinct features of a neural encoder of the
observations. We also introduce a new benchmark based on Atari 2600 games where
we evaluate representations based on how well they capture the ground truth
state variables. We believe this new framework for evaluating representation
learning models will be crucial for future representation learning research.
Finally, we compare our technique with other state-of-the-art generative and
contrastive representation learning methods. The code associated with this work
is available at this https URL

本论文提出了一种学习状态表示的方法，通过最大化观察神经编码器的空间和时间不同特征之间的相互信息来学习这些表示。此外，本文还介绍了一个基于 Atari 2600 游戏的新基准评估方法，用于评估它们能够捕捉地面真实状态变量的能力。我们相信这个新的评估框架会对未来的表示学习研究至关重要。最后，我们将我们的技术与其他最先进的生成和对比表示学习方法进行了比较。