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Updated: Jan 23, 2026

06:49
Phase Contrast and Differential Interference Contrast DIC Microscopy
Published on: August 6, 2008
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G2CL: グラデントガイド付きグラフ対照学習によるメッセージ対照的競合の解消
Shuai Zhang1, Shan Yang1, Wenyu Zhang1
1Zhejiang University of Finance and Economics, Hangzhou, 310018, China.
まとめ
本研究では、勾配ガイド付きグラフ対照学習(G2CL)を導入し、グラフ表現学習におけるメッセージ対照的競合(MCC)を解決します。G2CLは、ネガティブサンプルの類似性を向上させ、既存の方法を上回る性能を示します。
背景:
- InfoNCE損失を使用したグラフ対照学習法は、グラフ表現学習を進歩させてきました。
- 既存の方法は、InfoNCE損失とグラフニューラルネットワークのメッセージパッシングによるメッセージ対照的競合(MCC)に対処するのに苦労しており、ネガティブサンプルの類似性の最小化を妨げています。
結論:
- G2CLは、MCCを解決することにより、グラフ対照学習を大幅に改善します。
- G2CLの新しいコンポーネントは、偽ネガティブとLCEの効果的なソリューションを提供します。
- G2CLは、グラフ表現学習における新しい最先端のパフォーマンスベンチマークを設定します。
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