Linganisha mbinu

	Ujifunzaji wa Kulinganisha wa Kuona	Mtandao wa Makini wa Grafu
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2020	2018
Mwanzilishi	Chen, T. et al. (SimCLR); He, K. et al. (MoCo)	Veličković, P. et al.
Aina	Self-supervised deep representation learning	Graph neural network (attention-based)
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	4
Muhtasari	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).