Machine learningDeep learning / NLP / CV
可解释生成对抗网络
可解释生成对抗网络（Explainable GAN）将可解释性技术应用于生成对抗网络，以揭示哪些内部单元和潜在方向导致生成输出中的特定视觉或结构特征。它将 GAN 训练与事后分析工具（如单元解剖、显著性图或解耦潜在空间）相结合，使生成模型行为透明化和可审计。
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来源
- Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. (2019). GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In Proceedings of the International Conference on Learning Representations (ICLR 2019). link ↗
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. In Advances in Neural Information Processing Systems (NeurIPS 2014), 27. link ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Generative Adversarial Network. ScholarGate. https://scholargate.app/zh/deep-learning/explainable-gan
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