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desuetude    
n. 废止,不用

废止,不用

desuetude
n 1: a state of inactivity or disuse

Desuetude \Des"ue*tude\, n. [L. desuetudo, from desuescere, to
grow out of use, disuse; de suescere to become used or
accustomed: cf. F. d['e]su['e]tude. See {Custom}.]
The cessation of use; disuse; discontinuance of practice,
custom, or fashion.
[1913 Webster]

The desuetude abrogated the law, which, before, custom
had established. --Jer. Taylor.
[1913 Webster]

35 Moby Thesaurus words for "desuetude":
abandonment, absentation, abstention, abstinence, casting away,
cease, cessation, close, closing, closure, conclusion,
discontinuance, discontinuation, disuse, end, ending, evacuation,
forsaking, jettison, jettisoning, leaving, nonemployment,
nonprevalence, nonuse, obsolescence, obsoleteness, obsoletion,
obsoletism, pensioning off, pulling out, retirement,
superannuation, throwing overboard, unprevalence, withdrawal


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英文字典中文字典相关资料:


  • [1406. 2661] Generative Adversarial Networks - arXiv. org
    View a PDF of the paper titled Generative Adversarial Networks, by Ian J Goodfellow and 7 other authors
  • Generative adversarial nets | Proceedings of the 28th International . . .
    Abstract We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G
  • Generative Adversarial Nets - NIPS
    In this article, we explore the special case when the generative model generates samples by passing random noise through a multilayer perceptron, and the discriminative model is also a multilayer perceptron We refer to this special case as adversarial nets
  • 解读深度学习必读论文|Generative Adversarial Nets - 知乎
    Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio 本文提出了一种新的框架，通过对抗过程来估计生成模型。 在这个框架中，同时训练两个模型：一个生成模型G，用于学习并生成数据分布；和一个判别模型D，用于区分样本是来自真实数据还是生成模型G。 训练的目标是使判别模型D在判断样本来源时犯错的概率最大化，这可以被看作是一个最小最大（minimax）两人游戏。 在理论上，如果G和D是任意函数，那么存在一个唯一的解，使得G能够恢复训练数据的分布，而D在任何地方的值都等于1 2。
  • Generative Adversarial Nets - NIPS
    We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G
  • [PDF] Generative Adversarial Nets | Semantic Scholar
    This paper presents the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model, using Generative Adversarial Networks
  • [1406. 2661] Generative Adversarial Nets - ar5iv
    In this article, we explore the special case when the generative model generates samples by passing random noise through a multilayer perceptron, and the discriminative model is also a multilayer perceptron We refer to this special case as adversarial nets
  • proceedings. neurips. cc
    In this article, we explore the special case when the generative model generates samples\nby passing random noise through a multilayer perceptron, and the discriminative model is also a\nmultilayer perceptron We refer to this special case as adversarial nets





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