方法对比
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| 视觉对比学习× | 图注意力网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020 | 2018 |
| 提出者≠ | Chen, T. et al. (SimCLR); He, K. et al. (MoCo) | Veličković, P. et al. |
| 类型≠ | Self-supervised deep representation learning | Graph neural network (attention-based) |
| 开创性文献≠ | Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. ICML. link ↗ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| 别名≠ | Karşıtlık Öğrenmesi — Görsel (SimCLR / MoCo / BYOL), contrastive learning, self-supervised visual representation learning, SimCLR | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| 相关≠ | 5 | 4 |
| 摘要≠ | Visual contrastive learning is a self-supervised deep-learning approach — popularised by frameworks such as SimCLR (Chen et al., 2020) and MoCo (He et al., 2020) — that learns rich image representations without labels by pulling different augmentations of the same image together and pushing different images apart. It turns a large pool of unlabelled images into a useful feature extractor. | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). |
| ScholarGate数据集 ↗ |
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