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| شبكات الخصومة التوليدية القابلة للتفسير (Explainable GAN)× | شبكة الخصومة التوليدية× | |
|---|---|---|
| المجال | التعلم العميق | التعلم العميق |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2019 (GAN Dissection); ongoing | 2014 |
| صاحب الطريقة≠ | Bau, D. et al. (GAN Dissection); broader XAI-GAN community | Goodfellow, I. et al. |
| النوع≠ | Explainable generative model | Generative deep learning (adversarial two-network game) |
| المصدر التأسيسي≠ | 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. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| الأسماء البديلة | XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| ذات صلة | 4 | 4 |
| الملخص≠ | Explainable GAN applies interpretability techniques to Generative Adversarial Networks to reveal which internal units and latent directions cause specific visual or structural features in generated outputs. It combines GAN training with post-hoc analysis tools — such as unit dissection, saliency maps, or disentangled latent spaces — to make generative model behaviour transparent and auditable. | A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation. |
| ScholarGateمجموعة البيانات ↗ |
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