Computer Vision
AI and AutoML for Image-to-Image Translation Quality Assessment: Multi-Method Fusion in Computer Vision
- AI in computer vision
- AutoML image quality assessment
- machine learning GAN evaluation
- image-to-image translation
- DISTS perceptual metric
- Image Quality Assessment IQA
- gradient boosting computer vision
- MLJAR AutoML framework
- model monitoring for generative AI
- production AI validation
MLJAR tools were used in the following publication.
Paired Image-to-Image Translation Quality Assessment Using Multi-Method Fusion
Stefan Borasinski, Esin Yavuz, Sébastien Béhuret
Cyanapse Limited, Brighton, United Kingdom | ZEG.ai Ltd., London, United Kingdom
This research introduces an AI-driven Multi-Method Fusion (MMF) framework for automated quality assessment of image-to-image translation models in computer vision. By combining multiple Image Quality Assessment (IQA) metrics and training gradient-boosted ensembles (LightGBM, CatBoost, XGBoost) with MLJAR AutoML, the system predicts DISTS perceptual similarity scores without requiring ground truth images at inference time. The approach enables scalable, production-ready evaluation of GAN-generated images, including day-to-night and style-transfer tasks, achieving strong predictive correlation (R² up to 0.72). This work demonstrates how machine learning, ensemble modeling, and automated hyperparameter optimization can transform AI model monitoring and validation in real-world image synthesis pipelines.
arXiv • May 9, 2022
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