评估条件生成模型

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参考文献

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Schmidhuber, 2017 Advances in Neural Information Processing Systems, Vol. 30 DOI: 10.5591/978-1-57766-311-6.196 - 提出了Fréchet Inception距离（FID），这是广泛用于评估生成模型样本质量和多样性的指标，常用于条件评估。
Learning Transferable Visual Models From Natural Language Supervision, Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever, 2021 Proceedings of the 38th International Conference on Machine Learning (ICML), Vol. 139 - 介绍了CLIP模型，这是文本到图像和图像到文本任务的基础，也是衡量文本到图像生成中条件一致性的CLIP分数的基础。
Generative Adversarial Networks: A Survey and Taxonomy, Junxian Gui, Zhentao Liu, Jia-Wang Bian, Zi-Yi Dou, Jun-Yan He, Xun Gong, Xiao-Fei Zhang, Wen-Da Qiu, Yong-Wei De, Yi-Hang Shen, Ding-Han Shen, Jian-Jun Li, Zhi-Yan Liu, Li-Fu Zheng, Rui-Shan Liu, De-Wei Kong, Xiao-Jie Jin, Wei-Ming Li, Jing-Hao Zhou, Rui Xu, 2022 ACM Computing Surveys, Vol. 54 DOI: 10.1145/3459676 - 一项全面回顾生成对抗网络的调查，涵盖了其各个方面，包括专门的评估指标部分，为评估生成模型质量和多样性提供了广泛的背景，与条件模型相关。
Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Justin Johnson, Alexandre Alahi, Li Fei-Fei, 2016 European Conference on Computer Vision – ECCV 2016: Amsterdam, The Netherlands, October 8–16, 2016, Proceedings, Part IV (Springer, Cham) DOI: 10.1007/978-3-319-46475-6_43 - 提出了感知损失函数，利用预训练深度神经网络的特征来衡量图像相似度。这种方法适用于评估风格迁移或图像到图像转换等条件生成任务，其中结构和风格保真度是关键。