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Found 3 papers, 1 papers with code
Rethinking Weak Supervision in Helping Contrastive Learning
Therefore, to explore the mechanical differences between semi-supervised and noisy-labeled information in helping contrastive learning, we establish a unified theoretical framework of contrastive learning under weak supervision.
GBHT: Gradient Boosting Histogram Transform for Density Estimation
In this paper, we propose a density estimation algorithm called \textit{Gradient Boosting Histogram Transform} (GBHT), where we adopt the \textit{Negative Log Likelihood} as the loss function to make the boosting procedure available for the unsupervised tasks.
Leveraged Weighted Loss for Partial Label Learning
As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true.
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