Recent diffusion-based subject driven generative methods have enabled image
generations with good fidelity for specific objects or human portraits.
However, to achieve better versatility for applications, we argue that not only
improved datasets and evaluations are desired, but also more careful methods to
retrieve only relevant information from conditional images are anticipated. To
this end, we propose an anime figures dataset RetriBooru-V1, with enhanced
identity and clothing labels. We state new tasks enabled by this dataset, and
introduce a new diversity metric to measure success in completing these tasks,
quantifying the flexibility of image generations. We establish an RAG-inspired
baseline method, designed to retrieve precise conditional information from
reference images. Then, we compare with current methods on existing task to
demonstrate the capability of the proposed method. Finally, we provide baseline
experiment results on new tasks, and conduct ablation studies on the possible
structural choices.

通过提出一种基于扩散的主题驱动生成方法，使用动漫人物数据集 RetriBooru-V1 进行图像生成和多样性度量，对提升图像生成的灵活性进行研究和评估。

从参考图像中检索扩散模型的条件

Retrieving Conditions from Reference Images for Diffusion Models

Policy-Space Response Oracles (PSRO) is an influential algorithm framework
for approximating a Nash Equilibrium (NE) in multi-agent non-transitive games.
Many previous studies have been trying to promote policy diversity in PSRO. A
major weakness in existing diversity metrics is that a more diverse (according
to their diversity metrics) population does not necessarily mean (as we proved
in the paper) a better approximation to a NE. To alleviate this problem, we
propose a new diversity metric, the improvement of which guarantees a better
approximation to a NE. Meanwhile, we develop a practical and well-justified
method to optimize our diversity metric using only state-action samples. By
incorporating our diversity regularization into the best response solving in
PSRO, we obtain a new PSRO variant, Policy Space Diversity PSRO (PSD-PSRO). We
present the convergence property of PSD-PSRO. Empirically, extensive
experiments on various games demonstrate that PSD-PSRO is more effective in
producing significantly less exploitable policies than state-of-the-art PSRO
variants.

文章提出了一种新的 PSRO 算法变体，即 Policy Space Diversity PSRO（PSD-PSRO），通过一种新的多样性度量，它可以更有效地产生明显不易被利用的政策来近似 Nash 均衡。

非传递博弈的政策空间多样性

Policy Space Diversity for Non-Transitive Games

Collective insights from a group of experts have always proven to outperform
an individual's best diagnostic for clinical tasks. For the task of medical
image segmentation, existing research on AI-based alternatives focuses more on
developing models that can imitate the best individual rather than harnessing
the power of expert groups. In this paper, we introduce a single diffusion
model-based approach that produces multiple plausible outputs by learning a
distribution over group insights. Our proposed model generates a distribution
of segmentation masks by leveraging the inherent stochastic sampling process of
diffusion using only minimal additional learning. We demonstrate on three
different medical image modalities- CT, ultrasound, and MRI that our model is
capable of producing several possible variants while capturing the frequencies
of their occurrences. Comprehensive results show that our proposed approach
outperforms existing state-of-the-art ambiguous segmentation networks in terms
of accuracy while preserving naturally occurring variation. We also propose a
new metric to evaluate the diversity as well as the accuracy of segmentation
predictions that aligns with the interest of clinical practice of collective
insights.

该研究提出了一种利用扩散模型和随机采样方法生成多个可行的医学图像分割结果的方法，这种方法应用于 CT、超声波和 MRI 图像时，不仅能够提高分割准确度而且能够捕捉自然变异，同时还提出了一种度量分割结果多样性的新指标。

使用扩散模型进行模糊医学图像分割

Ambiguous Medical Image Segmentation using Diffusion Models

With increasing interest in procedural content generation by academia and
game developers alike, it is vital that different approaches can be compared
fairly. However, evaluating procedurally generated video game levels is often
difficult, due to the lack of standardised, game-independent metrics. In this
paper, we introduce two simulation-based evaluation metrics that involve
analysing the behaviour of an A* agent to measure the diversity and difficulty
of generated levels in a general, game-independent manner. Diversity is
calculated by comparing action trajectories from different levels using the
edit distance, and difficulty is measured as how much exploration and expansion
of the A* search tree is necessary before the agent can solve the level. We
demonstrate that our diversity metric is more robust to changes in level size
and representation than current methods and additionally measures factors that
directly affect playability, instead of focusing on visual information. The
difficulty metric shows promise, as it correlates with existing estimates of
difficulty in one of the tested domains, but it does face some challenges in
the other domain. Finally, to promote reproducibility, we publicly release our
evaluation framework.

本研究提出两种基于模拟的评估指标来测量生成游戏关卡的多样性和难度，并证明了该多样性指标比当前方法更稳健，并且能够直接影响可玩性因素，而难度指标则具有一定的相关性，为了推动可再生性，我们公开发布了我们的评估框架。