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Generalizing to unseen domains via PatchMix
Domain generalization (DG) aims to transfer knowledge learned from multiple source domains to unseen domains. One of the primary challenges hinders...
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PatchMix: patch-level mixup for data augmentation in convolutional neural networks
Convolutional neural networks (CNNs) have demonstrated impressive performance in fitting data distribution. However, due to the complexity in...
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Context-aware adaptive network for UDA semantic segmentation
Unsupervised Domain Adaptation (UDA) plays a pivotal role in enhancing the segmentation performance of models in the target domain by mitigating the...
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A transfer-based few-shot classification approach via masked manifold mixup and fuzzy memory contrastive learning
Few-shot learning studies the problem of classifying unseen images by learning only a small number of samples in these categories with the assistance...
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