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"Adaption"和"Adaptation"这两个词是相似但含义略有不同的单词。 "Adaption"是指一种生物或个体通过适应环境而发生的变化或改变。例如，狗的嗅觉灵敏度适应了狩猎的需要。 "Adaptation"则是指适应环境的过程或结果，是指生物或个体适应环境的行为或状态。
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8 de ago. de 2020 · Adaptation + Medallion trinket at the same time, or Medallion + Relentless trinket at the same time. The real problem is not the Sephuz legendary (although that’s a problem too) the problem is that these things are now not mutually exclusive (as far as we know). Also there are soulbinds and conduits with similar effects.
9 de ene. de 2021 · Human Racial Barely Useable. Using a CC-breaking trinket (Medallion/Adapatation) will put Will to Survive on a 90s cooldown. This means using Adaptation will make Will to Survive more or less useless, a rogues kidney will trigger Adaptation putting Will to Survive on 90s cooldown but Adaptation has a 60s cooldown.
26 de ene. de 2022 · Spektral-ravencrest January 27, 2022, 3:32am 2. Gingernutt: Unchained Aspirant’s Sigil Of Adaptation. Automatically triggered by any CC with a duration above a threshold. Gingernutt: Unchained Gladiator’s Medallion. Manually triggered (by you). These are two completely different trinkets and it’s easy to see why the Medallion is a much ...
adaptation适应 适合 adaption适合 改编 明显在改编这个意思上两者是有区别的 An adaptation of a composition. 乐曲的改编 Not assisting or promoting adaptation. 不利于适应的无助于或不能促进适应的 of or relating to adaptation. 适应的或者与适应有关的。 Marked by faulty or inadequate adaptation.
在Domain Adaptation中常用的分布距离度量包括Maximum Mean Discrepancy、Wasserstein distance等，这些分布距离度量用来衡量两个分布的差异大小。. 基于对抗学习的方法：同时训练feature extractor和一个discriminator，discriminator用来判断feature extractor生成的表示来自于哪个domain ...
adaptation 与 adaption都是n.名词,而用法上大多数是用adaptation,因为adaptation用在句子上后面跟of 或 to ,以下有两个例子. He made a quick adaptation to the new environment.他很快适应了新的环境. This play is an adaptation of a novel.这一剧本是由小说改编的. adaption n.
10 de jul. de 2024 · 关注. adaption和adaptation的区别在于它们的词性和使用场景有所不同。. 1. 词性不同. adaption是一个名词，主要用于描述一种适应或调整的状态。. 而adaptation也是一个名词，但其还可以作为形容词使用，既可以表示适应、调整的过程或结果，也可以用来描述某种适应 ...
Domain adaptation（DA: 域自适应），Domain generalization（DG: 域泛化）一直以来都是各大顶会的热门研究方向。. DA假设我们有有一个带标签的训练集（源域），这时候我们想让模型在另一个数据集上同样表现很好（目标域），利用目标域的无标签数据，提升模型在域间 ...
