English(EN) A Stochastic Latent Variable Generalization of Joint-Embedding Predictive Architectures

新的S-JEPA模型引入随机性以实现多样化的图像补全

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 05:00

研究人员推出了一种新颖的、用于掩码图像预测的联合嵌入预测架构（JEPA）的随机泛化模型S-JEPA。与先前估计单一补全的确定性方法不同，S-JEPA使用学习到的后验来推断随机潜在变量，从而能够生成多样化且语义各异的补全。这种新方法还提供了与误差相关的预测不确定性，这是确定性预测器所不具备的功能，同时不影响表示质量。 AI

影响引入了一种新颖的图像预测方法，有可能通过多样化的输出和不确定性估计来增强生成模型的能力。

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arXiv cs.CV TIER_1 English(EN) · Nikos Giakoumoglou, Paschalis Giakoumoglou, Tania Stathaki · 2026-11-09 05:00

联合嵌入预测架构的随机潜在变量泛化

arXiv:2611.09302v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) predict the latent embedding of a target signal from a context signal, with an auxiliary variable z conditioning the predictor. Despite its prominent role in the original JEPA bluep…

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联合嵌入预测架构的随机潜在变量泛化

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