Contrastive Learning Framework

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näkemys - Contrastive Learning Framework

Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation

The author proposes a Probabilistic Representation Contrastive Learning (PRCL) framework to enhance the robustness of unsupervised training in semi-supervised semantic segmentation by introducing probabilistic representations and global distribution prototypes.

PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation

提案されたPRCLフレームワークは、半教師ありセマンティックセグメンテーションにおけるロバストな対照学習プロセスを向上させます。

CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment

簡単で効果的な対照学習フレームワークを使用して、大規模言語モデルを人間の適合性に整える。

Contrastive Learning for Regression on Hyperspectral Data

Contrastive learning improves regression on hyperspectral data.

PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation

효율적인 반지도 학습을 위한 확률적 표현 대조 학습 프레임워크

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