השוואת שיטות
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| GAN בר-הסבר (Explainable GAN)× | מפענח אוטומטי וריאציוני× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2019 (GAN Dissection); ongoing | 2014 |
| הוגה השיטה≠ | Bau, D. et al. (GAN Dissection); broader XAI-GAN community | Kingma, D. P. & Welling, M. |
| סוג≠ | Explainable generative model | Deep generative latent-variable model (encoder–decoder) |
| מקור מכונן≠ | 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 ↗ |
| כינויים | XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| קשורות≠ | 4 | 5 |
| תקציר≠ | 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. |
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