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| 설명 가능한 변이형 오토인코더× | 자기 지도 변분형 오토인코더× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2013–2017 | 2014 (VAE); self-supervised variant ~2019–2021 |
| 창시자≠ | Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement) | Kingma, D. P. & Welling, M. (VAE); self-supervised extensions by various authors from ~2019 onward |
| 유형≠ | Generative model with interpretable latent space | Generative model with self-supervised representation learning |
| 원전 | Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014). link ↗ | Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014). link ↗ |
| 별칭 | XVAE, Interpretable VAE, Disentangled Variational Autoencoder, Explainable Generative Model | SS-VAE, self-supervised VAE, unsupervised VAE with self-supervised pretext tasks, contrastive VAE |
| 관련≠ | 4 | 6 |
| 요약≠ | An Explainable Variational Autoencoder (XVAE) extends the standard VAE framework with techniques that make its latent space interpretable: disentangling latent dimensions so each corresponds to a human-understandable factor, or post-hoc attribution methods (SHAP, integrated gradients) that trace reconstructions back to input features. It retains the VAE's generative power while adding transparency required in scientific and high-stakes applications. | A Self-supervised Variational Autoencoder (SS-VAE) combines the generative latent-space learning of a standard VAE with self-supervised pretext tasks — such as contrastive augmentation, masked reconstruction, or rotation prediction — to learn richer, more disentangled representations from unlabeled data without any manual annotation. |
| ScholarGate데이터셋 ↗ |
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