直接切片是不行的：

>>> a=[[1,2,3], [4,5,6]] >>> a[:, 0]                   # 嘗試用數(shù)組的方法讀取一列失敗 TypeError: list indices must be integers or slices, not tuple
1
2
3

我們可以直接構(gòu)造：

>>> b = [i[0] for i in a]     # 從a中的每一行取第一個元素。 >>> print(b) [1, 4]

Reference:

https://blog.csdn.net/xiaotao_1/article/details/80729458

     摘要: The classic about Generative Adversarial NetworksFirst paper [Generative Adversarial Nets] [Paper] [Code](the First paper of GAN)Unclassified [Deep Generative Image Models using a ...  閱讀全文

風(fēng)格遷移

1. PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup

（給人臉化妝的風(fēng)格轉(zhuǎn)移）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Chang_PairedCycleGAN_Asymmetric_Style_CVPR_2018_paper.pdf

 

2.CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

（將圖片轉(zhuǎn)化為卡通風(fēng)格的GAN）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf

 

3.StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

（人臉多種風(fēng)格轉(zhuǎn)換）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Choi_StarGAN_Unified_Generative_CVPR_2018_paper.pdf

 

4.Multi-Content GAN for Few-Shot Font Style Transfer

（字體風(fēng)格轉(zhuǎn)換）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Azadi_Multi-Content_GAN_for_CVPR_2018_paper.pdf

 

5.DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks

（圖到圖轉(zhuǎn)換）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Ma_DA-GAN_Instance-Level_Image_CVPR_2018_paper.pdf

 

6. Conditional Image-to-Image translation

（圖到圖的轉(zhuǎn)換）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Lin_Conditional_Image-to-Image_Translation_CVPR_2018_paper.pdf

 

圖片處理

1. DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

（去模糊）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Kupyn_DeblurGAN_Blind_Motion_CVPR_2018_paper.pdf

 

2.Attentive Generative Adversarial Network for Raindrop Removal from A Single Image

（去除圖片中的雨滴）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Qian_Attentive_Generative_Adversarial_CVPR_2018_paper.pdf

 

3. Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs

（用于照片增強(qiáng)）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Deep_Photo_Enhancer_CVPR_2018_paper.pdf

 

4. SeGAN: Segmenting and Generating the Invisible

（去遮擋）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Ehsani_SeGAN_Segmenting_and_CVPR_2018_paper.pdf

 

5.Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

（去陰影）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Stacked_Conditional_Generative_CVPR_2018_paper.pdf

 

6.Image Blind Denoising With Generative Adversarial Network Based Noise Modeling

（去噪聲）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Image_Blind_Denoising_CVPR_2018_paper.pdf

 

7. Single Image Dehazing via Conditional Generative Adversarial Network

（去噪聲）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Single_Image_Dehazing_CVPR_2018_paper.pdf

 

圖片生成

1. ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing

（空間轉(zhuǎn)換生成圖片）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Lin_ST-GAN_Spatial_Transformer_CVPR_2018_paper.pdf

 

2. SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

（由邊框生成圖片）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_SketchyGAN_Towards_Diverse_CVPR_2018_paper.pdf

 

3. TextureGAN: Controlling Deep Image Synthesis with Texture Patches

（由紋路生成圖片）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Xian_TextureGAN_Controlling_Deep_CVPR_2018_paper.pdf

 

4. Eye In-Painting with Exemplar Generative Adversarial Networks

（給人物畫眼睛）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Dolhansky_Eye_In-Painting_With_CVPR_2018_paper.pdf

 

5.Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network

（文本生成圖片）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Photographic_Text-to-Image_Synthesis_CVPR_2018_paper.pdf

 

6. Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks

（生成logo）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Sage_Logo_Synthesis_and_CVPR_2018_paper.pdf

 

7. Cross-View Image Synthesis Using Conditional GANs

（街區(qū)俯視圖和直視轉(zhuǎn)換）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Regmi_Cross-View_Image_Synthesis_CVPR_2018_paper.pdf

 

8. AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

（文本生成圖片）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_AttnGAN_Fine-Grained_Text_CVPR_2018_paper.pdf

 

9. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

（圖像高分辨率）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_High-Resolution_Image_Synthesis_CVPR_2018_paper.pdf

 

人臉相關(guān)

1. Finding Tiny Faces in the Wild with Generative Adversarial Network

（對低分辨率的人臉檢測）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Bai_Finding_Tiny_Faces_CVPR_2018_paper.pdf

 

2. Learning Face Age Progression: A Pyramid Architecture of GANs

（預(yù)測年齡）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Learning_Face_Age_CVPR_2018_paper.pdf

 

3. Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs

（對低分辨率人臉超分辨率）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Bulat_Super-FAN_Integrated_Facial_CVPR_2018_paper.pdf

4. Towards Open-Set Identity Preserving Face Synthesis

（人臉合成）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Bao_Towards_Open-Set_Identity_CVPR_2018_paper.pdf

 

5. Weakly Supervised Facial Action Unit Recognition through Adversarial Training

（人臉表情識別）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Peng_Weakly_Supervised_Facial_CVPR_2018_paper.pdf

 

6.FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis

（生成多角度人臉）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_FaceID-GAN_Learning_a_CVPR_2018_paper.pdf

 

7. UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition

（人臉生成）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Deng_UV-GAN_Adversarial_Facial_CVPR_2018_paper.pdf

 

8.Face Aging with Identity-Preserved Conditional Generative Adversarial Networks

（人臉老化）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Face_Aging_With_CVPR_2018_paper.pdf

 

人體相關(guān)

1. Deformable GANs for Pose-based Human Image Generation

（人物姿態(tài)遷移）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Siarohin_Deformable_GANs_for_CVPR_2018_paper.pdf

 

2. Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

(用GAN生成人行為軌跡追蹤)

http://openaccess.thecvf.com/content_cvpr_2018/papers/Gupta_Social_GAN_Socially_CVPR_2018_paper.pdf

 

3. GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB

（用GAN生成的手勢圖片做手勢追蹤的數(shù)據(jù)集）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Mueller_GANerated_Hands_for_CVPR_2018_paper.pdf

 

4. Multistage Adversarial Losses for Pose-Based Human Image Synthesis

（人體姿態(tài)合成）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Si_Multistage_Adversarial_Losses_CVPR_2018_paper.pdf

5. Disentangled Person Image Generation

（人體合成）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Ma_Disentangled_Person_Image_CVPR_2018_paper.pdf

 

domain adaptation

（這個沒來得及找了，可能轉(zhuǎn)行咯~ 唉）

1. Generate to Adapt: Aligning Domains Using Generative Adversarial Networks

 

2. Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation

 

3. Adversarial Feature Augmentation for Unsupervised Domain Adaptation

 

4. Domain Generalization With Adversarial Feature Learning

 

5. Image to Image Translation for Domain Adaptation

 

6. Duplex Generative Adversarial Network for Unsupervised Domain Adaptation

 

7. Conditional Generative Adversarial Network for Structured Domain Adaptation

 

目標(biāo)跟蹤檢測

1.Generative Adversarial Learning Towards Fast Weakly Supervised Detection

（弱監(jiān)督檢測）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_Generative_Adversarial_Learning_CVPR_2018_paper.pdf

 

2. SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation

（對抗學(xué)習(xí)生成軌跡樣本）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_SINT_Robust_Visual_CVPR_2018_paper.pdf

 

3. VITAL: VIsual Tracking via Adversarial Learning

http://openaccess.thecvf.com/content_cvpr_2018/papers/Song_VITAL_VIsual_Tracking_CVPR_2018_paper.pdf

 

GAN模型優(yōu)化

1. SGAN: An Alternative Training of Generative Adversarial Network

（替代訓(xùn)練GAN）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Chavdarova_SGAN_An_Alternative_CVPR_2018_paper.pdf

 

2. GAGAN: Geometry-Aware Generative Adversarial Networks

（一種關(guān)注幾何外形的GAN）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Kossaifi_GAGAN_Geometry-Aware_Generative_CVPR_2018_paper.pdf

 

3.Global versus Localized Generative Adversarial Nets

(局部優(yōu)化GAN)

http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Global_Versus_Localized_CVPR_2018_paper.pdf

 

4. Generative Adversarial Image Synthesis with Decision Tree Latent Controller

（決策樹）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Kaneko_Generative_Adversarial_Image_CVPR_2018_paper.pdf

 

5. Unsupervised Deep Generative Adversarial Hashing Network

（哈希GAN）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Dizaji_Unsupervised_Deep_Generative_CVPR_2018_paper.pdf

 

6. Multi-Agent Diverse Generative Adversarial Networks

（多個生成器GAN）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Ghosh_Multi-Agent_Diverse_Generative_CVPR_2018_paper.pdf

 

7. Duplex Generative Adversarial Network for Unsupervised Domain Adaptation

（雙鑒別器GAN）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Duplex_Generative_Adversarial_CVPR_2018_paper.pdf

 

圖像分割

1. Translating and Segmenting Multimodal Medical Volumes With Cycle- and Shape-Consistency Generative Adversarial Network

（圖像分割）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Translating_and_Segmenting_CVPR_2018_paper.pdf

 

行人重識別

1. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification

（用GAN生成的人體檢測的圖片）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Person_Transfer_GAN_CVPR_2018_paper.pdf

 

2. Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification

http://openaccess.thecvf.com/content_cvpr_2018/papers/Deng_Image-Image_Domain_Adaptation_CVPR_2018_paper.pdf

 

視覺特征提取

1. Visual Feature Attribution using Wasserstein GANs

http://openaccess.thecvf.com/content_cvpr_2018/papers/Baumgartner_Visual_Feature_Attribution_CVPR_2018_paper.pdf

 

域自適應(yīng)學(xué)習(xí)

1. Generate To Adapt: Aligning Domains using Generative Adversarial Networks

（視覺域自適應(yīng)）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Sankaranarayanan_Generate_to_Adapt_CVPR_2018_paper.pdf

 

圖像檢索

1. HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN

http://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_HashGAN_Deep_Learning_CVPR_2018_paper.pdf

 

遷移學(xué)習(xí)

1.Partial Transfer Learning With Selective Adversarial Networks

http://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_Partial_Transfer_Learning_CVPR_2018_paper.pdf

 

視頻生成

1. MoCoGAN: Decomposing Motion and Content for Video Generation

（用GAN生成視頻）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Tulyakov_MoCoGAN_Decomposing_Motion_CVPR_2018_paper.pdf

2. Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks

（生成延時視頻）

http://openaccess.thecvf.com/content_cvpr_2018/papers/Xiong_Learning_to_Generate_CVPR_2018_paper.pdf

 

小結(jié)：

  可以看出GAN相關(guān)的論文還不少呀，各個方面的都有，可是我個人覺得，可能沒有那種特別厲害的吧~hh

--------------------- 本文來自 眉間細(xì)雪 的CSDN 博客 ，全文地址請點(diǎn)擊：https://blog.csdn.net/weixin_42445501/article/details/82792311?utm_source=copy