방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 자가 지도 확산 모델× | 생성적 적대 신경망× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2020–2022 | 2014 |
| 창시자≠ | Ho, J. et al.; extended by Chen, T. et al. and subsequent self-supervised diffusion works | Goodfellow, I. et al. |
| 유형≠ | Generative model with self-supervised representation objective | Generative deep learning (adversarial two-network game) |
| 원전≠ | 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 ↗ |
| 별칭 | 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 |
| 관련≠ | 2 | 4 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|