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Found 4 papers, 1 papers with code
LINDA: Unsupervised Learning to Interpolate in Natural Language Processing
Despite the success of mixup in data augmentation, its applicability to natural language processing (NLP) tasks has been limited due to the discrete and variable-length nature of natural languages.
Learning Dynamic BERT via Trainable Gate Variables and a Bi-modal Regularizer
The BERT model has shown significant success on various natural language processing tasks.
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation
Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame.
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA
In this paper, we propose novel training schemes for multiple-choice video question answering with a self-supervised pre-training stage and a supervised contrastive learning in the main stage as an auxiliary learning.
