Σύγκριση μεθόδων

	Οπτική Συγκριτική Μάθηση	Δίκτυο Προσοχής Γραφήματος
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2020	2018
Δημιουργός	Chen, T. et al. (SimCLR); He, K. et al. (MoCo)	Veličković, P. et al.
Τύπος	Self-supervised deep representation learning	Graph neural network (attention-based)
Θεμελιώδης πηγή	Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. ICML.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
Εναλλακτικές ονομασίες	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
Συναφείς	5	4
Σύνοψη	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).