2024/02/26 · In this paper, we propose a novel Locally Weighted Graph Contrastive Learning method, named LocWGCL, while revealing that false hard negatives are primarily ...
2024/02/26 · Whether are more false hard negatives better? To answer these questions, in this paper, we propose a novel Locally Weighted Graph Contrastive ...
Abstract—Graph Contrastive Learning (GCL) has achieved great success in self-supervised representation learning through- out positive and negative pairs ...
Co-authors ; Seeking False Hard Negatives for Graph Contrastive Learning. Xin Liu, Biao Qian, Haipeng Liu, Yang Wang, Meng Wang. IEEE Transactions on Circuits ...
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
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An effective method is proposed to estimate the probability of a negative being true one, which constitutes a more suitable measure for negatives' hardness.
2021/10/05 · Unlike CL in other domains, most hard negatives are potentially false negatives (negatives that share the same class with the anchor) if they ...
含まれない: Seeking | 必須にする:Seeking
By contrasting positive-negative counterparts, graph contrastive learning has become a prominent technique for unsupervised graph representation learning.
7 日前 · Abstract—Graph Contrastive Learning (GCL) seeks to learn nodal or graph representations that contain maximal consistent information from.
2023/08/04 · We propose HomoGCL, a model-agnostic framework to expand the positive set using neighbor nodes with neighbor-specific significances.
Debiased graph contrastive learning based on positive and unlabeled learning ... Seeking False Hard Negatives for Graph Contrastive Learning. Article. Aug 2024.