通过食物理解扩散概念代数的局限性
Understanding the Limitations of Diffusion Concept Algebra Through Food
AI总结：
本文以食物图像为切入点，分析扩散模型概念遍历技术的定性与定量表现，揭示其在捕捉烹饪多样性方面的能力及偏差与局限性。
摘要：
图像生成技术，尤其是潜在扩散模型，近年来迅速流行。许多技术被开发出来以操纵和澄清这些大规模模型学习到的语义概念，为理解偏差和概念关系提供了关键见解。然而，这些技术通常只在人类或动物面部以及艺术风格转换等常规领域得到验证。食物领域通过复杂的构图和区域性偏差带来了独特挑战，这可以揭示现有方法中的局限性与机遇。通过食物图像的视角，我们分析了一种概念遍历技术中的定性与定量模式。我们揭示了关于模型捕捉和表示烹饪多样性细微差别的能力的可测量见解，同时也识别出模型偏差和局限性显现的领域。
英文摘要：
Image generation techniques, particularly latent diffusion models, have exploded in popularity in recent years. Many techniques have been developed to manipulate and clarify the semantic concepts these large-scale models learn, offering crucial insights into biases and concept relationships. However, these techniques are often only validated in conventional realms of human or animal faces and artistic style transitions. The food domain offers unique challenges through complex compositions and regional biases, which can shed light on the limitations and opportunities within existing methods. Through the lens of food imagery, we analyze both qualitative and quantitative patterns within a concept traversal technique. We reveal measurable insights into the model's ability to capture and represent the nuances of culinary diversity, while also identifying areas where the model's biases and limitations emerge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
