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English(EN) Variance-reduced Domain Adaptation using Paired Sampling

新研究解决域适应技术中的方差减少问题 · 跟踪到2个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文提出了减少域适应技术中方差的方法。第一篇论文“使用配对采样进行方差减小的域适应”（PSDA）引入了一种随机方差减小（SVR）技术，该技术将域内和跨域的观测值配对，以最小化梯度方差。第二篇论文“流数据域适应的在线方差减小”提出了ARROW，一种专为流数据设计的自适应SVR算法，该算法维护移动平均参考并自适应地重新加权小批量。 AI

影响 这些方法旨在提高机器学习模型在处理不同分布数据时的准确性和效率。

排序理由 两篇在arXiv上发表的学术论文，提出了域适应的新方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新研究解决域适应技术中的方差减少问题 · 跟踪到2个来源

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两篇在arXiv上发表的学术论文，提出了域适应的新方法。
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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Andrea Napoli ·

    Paired Sampling 驱动的方差减小域自适应

    arXiv:2607.20367v1 Announce Type: new Abstract: Correlation alignment and the maximum mean discrepancy are two widely used distribution-matching frameworks for unsupervised domain adaptation (UDA). However, high variance in these losses has been shown to undermine their effective…

  2. arXiv cs.LG TIER_1 English(EN) · Andrea Napoli ·

    面向流式数据的域自适应在线方差缩减

    arXiv:2607.20374v1 Announce Type: new Abstract: This paper studies the problem of stochastic variance reduction (SVR) for the maximum mean discrepancy (MMD) and correlation alignment (CORAL) loss functions. Although various offline SVR algorithms for these losses have been propos…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    使用配对采样实现方差减小的域自适应

    Correlation alignment and the maximum mean discrepancy are two widely used distribution-matching frameworks for unsupervised domain adaptation (UDA). However, high variance in these losses has been shown to undermine their effectiveness in minibatch optimisation settings. Further…

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