AMEAN applies adversarial meta-learning to discover implicit meta-sub-target clusters in blended target data, reducing intra-target category misalignment and outperforming standard DA methods on three BTDA benchmarks.
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Randomly masking square regions of input images during CNN training yields new state-of-the-art test errors of 2.56% on CIFAR-10, 15.20% on CIFAR-100, and 1.30% on SVHN.
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Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
AMEAN applies adversarial meta-learning to discover implicit meta-sub-target clusters in blended target data, reducing intra-target category misalignment and outperforming standard DA methods on three BTDA benchmarks.
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Improved Regularization of Convolutional Neural Networks with Cutout
Randomly masking square regions of input images during CNN training yields new state-of-the-art test errors of 2.56% on CIFAR-10, 15.20% on CIFAR-100, and 1.30% on SVHN.