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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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Overview
In the final lesson of Practical Deep Learning for Coders we'll study one of the most important techniques in modern architectures: the skip connection. This is most famously used in the res…
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Is this the official open source code for the paper '“IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection”
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### Team Name:
Penn Ave Fish Company
### Project Description:
#### Introduction
A prerequisite for quantum algorithms to outperform their classical counterparts lies in the ability to ef…
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Institute: Facebook
URL: https://arxiv.org/pdf/1712.03999.pdf
Keyword: GAN, Eye-In-Painting
Interest: 3
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Dear Yunjey Choi,
I read your CVPR paper, "StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation", in which you refer to using Amazon Mechanical Turk (A…
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These days, 'Domain-Adversarial Training of Neural Networks' (https://arxiv.org/abs/1505.07818) is getting attention in Generative Adversarial Networks and domain adaptation learning.
It's already im…
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Generative adversarial networks description
mhrmm updated
4 years ago
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Traceback (most recent call last):
File "D:/python file/Super-Resolution-using-Generative-Adversarial-Networks-master/models.py", line 10, in
from keras_ops import fit as bypass_fit, smooth_g…
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# 참고 홈페이지
워낙 유명한 논문이기에 잘 설명된 reveiw를 참조하였습니다.
[ Link 1. ](https://ysbsb.github.io/gan/2020/12/05/DCGAN.html)
[ Link 2. ](https://blog.naver.com/laonple/221201915691)
# Abstract
- CNN은 지도 학습에서 뛰…