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A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction
In this paper, we investigate the effectiveness of contrastive learning methods for predicting grasp outcomes in an unsupervised manner.
Using contrastive learning to improve the performance of steganalysis schemes
To decrease the computing complexity of the contrastive loss in supervised learning, we design a novel Steganalysis Contrastive Loss (StegCL) based on the equivalence and transitivity of similarity.
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