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Generative models, such as Generative Adversarial Networks (GANs), are a class of deep learning models designed to generate new, previously unseen data similar to the training data.
Data augmentation is a technique for artificially increasing the size and diversity of a training dataset by creating modified versions of existing data. This is especially common in machine learning fields like computer vision to improve model performance.
Domain adaptation is a technique used to adapt a model trained on one domain (aka, source domain) to another domain (aka, target domain) where the data distribution is different. This research aims to develop domain adaptation techniques.
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