为ESRGAN添加不确定性估计，让超分辨率结果更可信。
Uncertainty Estimation for Super-Resolution using ESRGAN
- 用蒙特卡洛丢弃和深度集成方法估算预测不确定性
- 估计结果校准良好，且不降低模型性能
- 适合需要判断输出可靠性的人群使用
基于深度学习的图像超分辨率技术在生成对抗网络助力下迅速发展，SRGAN与ESRGAN等模型持续位列顶尖。然而，这些模型缺乏可靠的预测不确定性估计方法。本文通过引入蒙特卡洛丢弃与深度集成技术，增强这些模型以计算预测不确定性。结合预测结果，不确定性估计可提示用户哪些像素的输出可能存在误差，从而提升可靠性。实验表明，该不确定性估计具备良好的校准性，且未带来性能损失。
原文摘要 · Abstract (English)
Deep Learning-based image super-resolution (SR) has been gaining traction with the aid of Generative Adversarial Networks. Models like SRGAN and ESRGAN are constantly ranked between the best image SR tools. However, they lack principled ways for estimating predictive uncertainty. In the present work, we enhance these models using Monte Carlo-Dropout and Deep Ensemble, allowing the computation of predictive uncertainty. When coupled with a prediction, uncertainty estimates can provide more information to the model users, highlighting pixels where the SR output might be uncertain, hence potentially inaccurate, if these estimates were to be reliable. Our findings suggest that these uncertainty estimates are decently calibrated and can hence fulfill this goal, while providing no performance drop with respect to the corresponding models without uncertainty estimation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。