Several machine learning (ML) applications are characterized by searching for
an optimal solution to a complex task. The search space for this optimal
solution is often very large, so large in fact that this optimal solution is
often not computable. Part of the problem is that many candidate solutions
found via ML are actually infeasible and have to be discarded. Restricting the
search space to only the feasible solution candidates simplifies finding an
optimal solution for the tasks. Further, the set of feasible solutions could be
re-used in multiple problems characterized by different tasks. In particular,
we observe that complex tasks can be decomposed into subtasks and corresponding
skills. We propose to learn a reusable and transferable skill by training an
actor to generate all feasible actions. The trained actor can then propose
feasible actions, among which an optimal one can be chosen according to a
specific task. The actor is trained by interpreting the feasibility of each
action as a target distribution. The training procedure minimizes a divergence
of the actor's output distribution to this target. We derive the general
optimization target for arbitrary f-divergences using a combination of kernel
density estimates, resampling, and importance sampling. We further utilize an
auxiliary critic to reduce the interactions with the environment. A preliminary
comparison to related strategies shows that our approach learns to visit all
the modes in the feasible action space, demonstrating the framework's potential
for learning skills that can be used in various downstream tasks.

利用可行解来简化机器学习中复杂任务的寻优问题，利用可行 action 子空间的训练可以生成多个可重用技巧，通过使用核密度估计、重采样和重要性采样的组合来推导任意 f - 分歧的一般优化目标。

学习生成所有可行的动作

Learning to Generate All Feasible Actions

Recently introduced Contrastive Language-Image Pre-Training (CLIP) bridges
images and text by embedding them into a joint latent space. This opens the
door to ample literature that aims to manipulate an input image by providing a
textual explanation. However, due to the discrepancy between image and text
embeddings in the joint space, using text embeddings as the optimization target
often introduces undesired artifacts in the resulting images. Disentanglement,
interpretability, and controllability are also hard to guarantee for
manipulation. To alleviate these problems, we propose to define corpus
subspaces spanned by relevant prompts to capture specific image
characteristics. We introduce CLIP Projection-Augmentation Embedding (PAE) as
an optimization target to improve the performance of text-guided image
manipulation. Our method is a simple and general paradigm that can be easily
computed and adapted, and smoothly incorporated into any CLIP-based image
manipulation algorithm. To demonstrate the effectiveness of our method, we
conduct several theoretical and empirical studies. As a case study, we utilize
the method for text-guided semantic face editing. We quantitatively and
qualitatively demonstrate that PAE facilitates a more disentangled,
interpretable, and controllable image manipulation with state-of-the-art
quality and accuracy.

使用 CLIP Projection-Augmentation Embedding（PAE）作为优化目标，以改善文本引导的图像操作的性能。

CLIP-PAE: 投影增强嵌入以提取相关特征，以实现可分解、可解释和可控的文本引导图像操纵

CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable, and Controllable Text-Guided Image Manipulation