IJCAI 2021. abstract inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation(蒸馏) with multi-scale contrastive learni...
论文标题:Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning论文作者:Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan论文来源:2021, IJCAI论文地址:download 论文代码:download 1 Introduction...
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning 爱吃鱼的猫 4 人赞同了该文章 近年来基于对比学习的图神经网络技(GCL)术取得了巨大成功,其可以有效减少图神经网络对有标签数据的依赖,但是目前的研究中仍存在一下限制:1)现有的基于MI的对比方法需要计算正负样本对得分,导致...
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Contrastive learning is the most cutting-edge type of self-supervised learning framework. The basic idea of contrastive learning is that different transformations of a sample image have similar representations and these representations should be different from the different sample images. The unlabeled dat...
proposed a collaborative approach that integrated contrastive learning with a fine-tuned pre-trained ConvNet encoder to capture unbiased feature representations [34]. In [35], Beel et al. proposed a Siamese neural network architecture for automatic algorithm selection, which focused more on “alike ...
UpdatedDec 21, 2021 Python aheuillet/NASiam Star6 Code Issues Pull requests Official implementation of the NASiam paper. computer-visionneural-architecture-searchsiamese-networkscontrastive-learning UpdatedFeb 2, 2023 Python bghojogh/Offline-Online-Triplet-Mining ...
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In this paper, we tackle this problem by introducing a simple but effective contrastive learning framework. The key insight is to employ siamese-style metric loss to match intra-prototype features, while increasing the distance between inter-prototype features. We conduct extensive experiments on ...