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arXiv · cs.AI · Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah · 11 小时前模型评分41

CAT-CKD：面向皮肤癌诊断多源无源域适应的对比适配器训练与共识知识蒸馏

Contrastive Adapter Training and Consensus Knowledge Distillation for Multi-Source-Free Domain Adaptation in Skin Cancer Diagnosis

导读

研究提出 CAT-CKD 方法，用于皮肤癌诊断中的多源无源无监督域适应（MSFDA，Multi-Source-Free Unsupervised Domain Adaptation），在冻结的 ViT 主干内用监督对比学习训练轻量 ConvPass 适配器，并通过共识知识蒸馏聚合多个源模型输出。

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中 · 待人工复核

  • 只有单一媒体来源，需要人工复核。

来源级别：媒体与个人

应用领域：医疗与生命科学

事件类型：研究发布

证据摘录

  • “To address these limitations, we propose CAT-CKD, consisting of two components: (1) contrastive adapter training (CAT), which trains lightweight ConvPass adapters within a frozen ViT backbone using supervised contrastive learning (SCL) to establish a shared domain-invariant feature space before source-specific model training, and (2) consensus knowledge distillation (CKD), which aggregates logits from multiple source models into a consensus supervisory signal and adapts a student model on unlabeled target data using KL divergence.”
  • “The code is available at this https URL .”

来源：arXiv · cs.AI · arxiv.org

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