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### 論文へのリンク
[[arXiv:1906.01529] Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy](https://arxiv.org/abs/1906.01529)
### 著者・所属機関
Zhengwei Wang, Qi She, Tomas E. Ward
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Hi...
I was wondering if adding Wasserstein distance to the code would help the GAN to stabilize and give better results... from what I've read, it has really nice properties and implementing it sh…
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# Abstract
Generative Adversarial Networks(GAN)은 데이터 생성에서 뛰어난 모습을 보이고 있다. 많은 영역에서 쓰이고 있지만 여전히 안정적인 학습에는 어려움이 따른다. 문제점으로는 Nash-equilibrium, internal covariate shift, mode collapse, vanishing gradient,…
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# What
Create a pipeline using as reference Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., & Aila, T. (2020). Training generative adversarial networks with limited data. Advances in n…
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https://doi.org/10.1101/390153
> A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethica…
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https://openreview.net/pdf?id=padYzanQNbg this paper is something we might want to add @frankschae .
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**conditional VAEで音声embeddingを獲得し、転写することで音声変換を行う。更にWGANを使ってクリアな音声を目指す。**
論文本体・著者
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* Chin-Cheng Hsu, Hsin-Te Hwang, Yi-Chiao Wu, Yu Tsao, Hsin-Min Wang
* arXiv: https://arxiv.or…
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Hello,
For the cross correlation loss, I was wondering why there was an extra factor of 10 in this function:
def compute(self, x_fake):
cross_correl_fake = cacf_torch(self.transform(x_f…
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Now we have a forward model $f(\vec{\theta})$ (an NN emulator) which could return the SED of given physical parameters. Then we need to build a neural density estimator to describe the $P(\vec{\theta}…