Defining and Quantifying Creative Behavior in Popular Image Generators
Creativity of generative AI models has been a subject of scientific debate in the last years, without a conclusive answer. In this paper, we study creativity from a practical perspective and introduce quantitative measures that help the user to choose a suitable AI model for a given task. We evaluated our measures on a number of popular image-to-image generation models, and the results of this suggest that our measures conform to human intuition.
Code (0)
등록된 구현이 없습니다.
Tasks
Image GenerationSimilar Papers 제목 키워드 기반
A Machine Learning Approach for Evaluating Creative Artifacts
Much work has been done in understanding human creativity and defining measures to evaluate creativity. This is necessary mainly for the reason of having an objective and automatic way of quantifying creative artifacts. …
BIG-bench Machine LearningRedefining <Creative> in Dictionary: Towards an Enhanced Semantic Understanding of Creative Generation
``Creative'' remains an inherently abstract concept for both humans and diffusion models. While text-to-image (T2I) diffusion models can easily generate out-of-domain concepts like ``a blue banana'', they struggle with g…
Benchmarking Language Model Creativity: A Case Study on Code Generation
As LLMs become increasingly prevalent, it is interesting to consider how ``creative'' these models can be. From cognitive science, creativity consists of at least two key characteristics: \emph{convergent} thinking (purp…
BenchmarkingCode GenerationLanguage ModelingLanguage ModellingCARLoS: Retrieval via Concise Assessment Representation of LoRAs at Scale
The rapid proliferation of generative components, such as LoRAs, has created a vast but unstructured ecosystem. Existing discovery methods depend on unreliable user descriptions or biased popularity metrics, hindering us…
Image GenerationCreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation
Large Language Models (LLMs) have proved effective and efficient in generating code, leading to their utilization within the hardware design process. Prior works evaluating LLMs' abilities for register transfer level cod…
Code Generation