Evaluating Generative Models and Individual Generated Images Based on Complexity and Vulnerability
The core message of this article is that the representation space around generated images exhibits distinct properties compared to real images, specifically in terms of complexity and vulnerability. The authors propose two novel metrics, anomaly score (AS) and anomaly score for individual images (AS-i), to effectively evaluate generative models and individual generated images based on these properties.
Generative Model Evaluation for Realistic Image Synthesis in OCR Systems
프레셋 인셉션 거리(FID) 점수를 통해 실제 이미지와 생성된 이미지의 품질을 평가합니다.
Advancing Generative Model Evaluation: A Novel Algorithm for Realistic Image Synthesis and Comparison in OCR System
The authors introduce a pioneering algorithm to objectively assess the realism of synthetic images, enhancing evaluation methodology by refining the Fréchet Inception Distance (FID) score. This breakthrough enables comparison of generative models and sets a new standard in image generation evaluation.