Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Visueel contrastief leren× | Graph Attention Network× | |
|---|---|---|
| Vakgebied | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2020 | 2018 |
| Grondlegger≠ | Chen, T. et al. (SimCLR); He, K. et al. (MoCo) | Veličković, P. et al. |
| Type≠ | Self-supervised deep representation learning | Graph neural network (attention-based) |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen≠ | 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 |
| Verwant≠ | 5 | 4 |
| Samenvatting≠ | 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). |
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