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Contrastive Learning for Unsupervised Data Mining: A New Paradigm in Self-Supervised Learning
- Contrastive Learning is a promising approach in self-supervised learning.
- Data augmentation is applied to create multiple views of the same data point.
- A contrastive loss function is used to optimize the model's learning.
- Contrastive learning has applications in computer vision, NLP, and graph-based data.
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