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| شبكات الخصومة التوليدية القابلة للتفسير (Explainable GAN)× | المشفّر التلقائي التبايني× | |
|---|---|---|
| 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 | Kingma, D. P. & Welling, M. |
| النوع≠ | Explainable generative model | Deep generative latent-variable model (encoder–decoder) |
| 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 ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| الأسماء البديلة | XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| ذات صلة≠ | 4 | 5 |
| 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. | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. |
| ScholarGateمجموعة البيانات ↗ |
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