השוואת שיטות
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| מודל דיפוזיה עם למידה עצמית× | רשת יריבות יוצרת (Generative Adversarial Network)× | מפענח אוטומטי וריאציוני× | |
|---|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning | Machine learning |
| שנת המקור≠ | 2020–2022 | 2014 | 2014 |
| הוגה השיטה≠ | Ho, J. et al.; extended by Chen, T. et al. and subsequent self-supervised diffusion works | Goodfellow, I. et al. | Kingma, D. P. & Welling, M. |
| סוג≠ | Generative model with self-supervised representation objective | Generative deep learning (adversarial two-network game) | Deep generative latent-variable model (encoder–decoder) |
| מקור מכונן≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| כינויים | SSDM, self-supervised score-based model, diffusion-based self-supervised learning, denoising diffusion with self-supervised pretraining | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| קשורות≠ | 2 | 4 | 5 |
| תקציר≠ | A self-supervised diffusion model couples the iterative noise-and-denoise generative process of denoising diffusion probabilistic models with a self-supervised representation learning objective — such as contrastive or masked prediction loss — so that the model simultaneously learns to generate realistic data and to produce semantically meaningful representations without any labeled examples. | 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. | 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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