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Adaptive Data Augmentation with Deep Parallel Generative Models
Data augmentation(DA) is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce.
Achieving Fairness in Determining Medicaid Eligibility through Fairgroup Construction
Effective complements to human judgment, artificial intelligence techniques have started to aid human decisions in complicated social problems across the world.
Deep Generative Inpainting with Comparative Sample Augmentation
Recent advancements in deep learning techniques such as Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN) have achieved breakthroughs in the problem of semantic image inpainting, the task of reconstructing missing pixels in given images.
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