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Dataset augmentation involves creating synthetic training data from existing data to improve model generalization, particularly in classification tasks. Semi-supervised learning combines labeled and unlabeled data to enhance learning, focusing on clustering similar examples in representation space. Additionally, generative and discriminative models can share parameters to optimize both supervised and unsupervised learning objectives.

Author

Bhvana Mr

Language

EN