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Found 4 papers, 3 papers with code
FLD+: Data-efficient Evaluation Metric for Generative Models
We introduce a new metric to assess the quality of generated images that is more reliable, data-efficient, compute-efficient, and adaptable to new domains than the previous metrics, such as Fr\'echet Inception Distance (FID).
Evaluation Metric for Quality Control and Generative Models in Histopathology Images
Our study introduces ResNet-L2 (RL2), a novel metric for evaluating generative models and image quality in histopathology, addressing limitations of traditional metrics, such as Frechet inception distance (FID), when the data is scarce.
Normalizing Flow-Based Metric for Image Generation
This property gives the proposed metrics a few advantages over the widely used Fr\'echet inception distance (FID) and other recent metrics.
Ranked #1 on
Image Generation
on CelebA-HQ
WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency
Recent advancements in single image super-resolution have been predominantly driven by token mixers and transformer architectures.
Ranked #1 on
Image Super-Resolution
on BSD100 - 2x upscaling
