方法对比
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| 基于得分的生成模型× | 胶囊网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019 | 2017 |
| 提出者≠ | Song, Y. & Ermon, S. | Sabour, S., Frosst, N. & Hinton, G. E. |
| 类型≠ | Score-based generative model (SDE framework) | Deep learning architecture (vector capsules with dynamic routing) |
| 开创性文献≠ | Song, Y. & Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 32, 11895–11907. link ↗ | Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS). link ↗ |
| 别名 | Skor Tabanlı Üretici Model (Score-Based / SDE), score-based diffusion, SDE-based generative model, score SDE | Kapsül Ağı (CapsNet), CapsNet, capsule net, dynamic routing network |
| 相关≠ | 5 | 4 |
| 摘要≠ | A score-based generative model, introduced by Yang Song and Stefano Ermon in 2019 and generalized to the stochastic differential equation (SDE) framework in 2021, learns the gradient of the data density — the score — rather than predicting noise directly, and uses it to generate new samples. It is the mathematical generalization that unifies diffusion models under a continuous-time formulation. | A Capsule Network (CapsNet) is a deep learning architecture introduced by Sara Sabour, Nicholas Frosst and Geoffrey Hinton in 2017 that organises neurons as vectors (capsules) rather than scalar activations, so that spatial hierarchy and pose (orientation) information are encoded directly. It was proposed to overcome the fragility of convolutional networks to changes in viewpoint. |
| ScholarGate数据集 ↗ |
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