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Hierarchical Modes Exploring in Generative Adversarial Networks
In conditional Generative Adversarial Networks (cGANs), when two different initial noises are concatenated with the same conditional information, the distance between their outputs is relatively smaller, which makes minor modes likely to collapse into large modes.
Exploring Bias in GAN-based Data Augmentation for Small Samples
For machine learning task, lacking sufficient samples mean the trained model has low confidence to approach the ground truth function.
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