次に読む論文

[soraKING44]

Improved Techniques for Training GANs

https://github.com/soraKING44/survey_paper/tree/GAN/GAN/Improved_Techniques_for_Training_GANs

これに出てて気になったやつ

https://arxiv.org/abs/1610.01945 https://arxiv.org/abs/1610.09585 https://arxiv.org/abs/1609.03126

[soraKING44]

Deep planning network

https://arxiv.org/abs/1811.04551 https://planetrl.github.io/

[soraKING44]

https://www.slideshare.net/cvpaperchallenge/gan-133159239

これで紹介されていて気になったやつ

AlexNet http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networ VGGNet https://arxiv.org/abs/1409.1556 GoogLeNet https://ieeexplore.ieee.org/document/7298594 ResNet https://ieeexplore.ieee.org/document/7780459 BigGAN https://arxiv.org/abs/1809.11096 CycleGAN http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.pdf ACGAN https://arxiv.org/abs/1610.09585 SNGAN https://arxiv.org/abs/1802.05957 Self-Attention GAN https://arxiv.org/abs/1805.08318 評価手法 https://arxiv.org/abs/1706.08500 https://arxiv.org/abs/1807.09499 GANの調査研究 https://papers.nips.cc/paper/7350-are-gans-created-equal-a-large-scale-study SimGAN https://ieeexplore.ieee.org/document/8099724 GraspGAN https://ieeexplore.ieee.org/document/8460875 学習サンプル生成 http://openaccess.thecvf.com/content_ECCV_2018/papers/Tal_Remez_Learning_to_Segment_ECCV_2018_paper.pdf

[soraKING44]

CVPR2019 めっちゃnoisyなデータセットでもGANできる話 https://arxiv.org/abs/1811.11165 物体検出でドメイン適応する話 https://arxiv.org/abs/1812.04798

[soraKING44]

SLAMのまとめ https://ssk0109.hatenablog.com/entry/2018/12/30/063109 あとで記事を読む

[soraKING44]

RNNの関連研究読みたい https://qiita.com/kiminaka/items/87afd4a433dc655d8cfd

[soraKING44]

http://www.deeplearningbook.org/

GoodfellowのDeep Learning