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【吴恩达团队】最新生成对抗网络教程

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2020-10-08 18:13:01

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coursera.org 应粉丝私信搬来的视频，该系列一共3个课程，目前只出了前两课；字幕会慢慢加上，没看到字幕的话则是还没处理到；主页有很多吴恩达团队近些年的视频教学；人工智能技术交流群：78174903，不定期分享干货，点赞关注一起进步啦。

啥都会一点的研究生

deeplearning.ai

生成对抗网络

啥都会一点的研究生发消息

啥都会一点，啥都不精通，自动驾驶感知算法工程师。公众号：啥都会一点的研究生

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第一课：1.1welcome-to-the-specialization

05:23

1.2welcome-to-week-1

00:56

1.3generative-models

08:30

1.4real-life-gans

05:15

1.5intuition-behind-gans

05:23

1.6discriminator

05:55

1.7generator

07:07

1.8bce-cost-function

06:37

1.9putting-it-all-together

05:48

1.10optional-intro-to-pytorch

06:47

2.1welcome-to-week-2

00:51

2.2activations-basic-properties

04:16

2.3common-activation-functions

06:10

2.4batch-normalization-explained

05:56

2.5batch-normalization-procedure

05:15

2.6review-of-convolutions

03:15

2.7padding-and-stride

03:50

2.8pooling-and-upsampling

05:07

2.9transposed-convolutions

02:51

3.1welcome-to-week-3

01:46

3.2mode-collapse

04:41

3.3problem-with-bce-loss

03:57

3.4earth-movers-distance

02:19

3.5wasserstein-loss

04:46

3.6condition-on-wasserstein-critic

03:20

3.7 1-lipschitz-continuity-enforcement

05:47

4.1welcome-to-week-4

01:11

4.2conditional-generation-intuition

02:59

4.3conditional-generation-inputs

04:55

4.4controllable-generation

03:05

4.5vector-algebra-in-the-z-space

03:59

4.6challenges-with-controllable-generation

02:45

4.7classifier-gradients

02:01

4.8disentanglement

04:45

4.9conclusion-of-course-1

01:09

第二课：1.1welcome-to-course-2

03:55

1.2_welcome-to-week1

01:22

1.3evaluation

06:21

1.4comparing-images

03:58

1.5feature-extraction

06:58

1.6inception-v3-and-embeddings

06:16

1.7frechet-inception-distance-fid

14:38

1.8inception-score

10:03

1.9sampling-and-truncation

06:46

1.10precision-and-recall

05:47

2.1welcome-to-week 2

01:14

2.2disadvantages-of-gans

04:54

2.3alternatives-to-gans

06:17

2.4intro-to-machine-bias

06:45

2.5defining-fairness

02:19

2.6ways-bias-is-introduced

07:00

3.1welcome-to-week 3

00:54

3.2gan-improvements

07:03

3.3stylegan-overview

08:33

3.4progressive-growing

05:33

3.5noise-mapping-network

06:19

3.6adaptive-instance-normalization-adain

08:09

3.7style-and-stochastic-variation

08:06

3.8putting-it-all-together

02:34

3.9works-cited_2019-03-16-stylegan-facestraining

01:00

第三课：1.1welcome-to-course-3

02:47

1.2welcome-to-week-1

01:16

1.3overview-of-gan-applications

06:28

1.4data-augmentation-methods-and-uses

05:24

1.5data-augmentation-pros-cons

02:59

1.6gans-for-privacy

04:09

1.7gans-for-anonymity

01:59

2.1welcome-to-week-2

00:51

2.2image-to-image-translation

05:21

2.3pix2pix-overview

04:58

2.4pix2pix-patchgan

01:32

2.5pix2pix-u-net

08:38

2.6pix2pix-pixel-distance-loss-term

03:11

2.7pix2pix-putting-it-all-together

02:43

2.8pix2pix-advancements

02:22

3.1welcome-to-week-3

00:58

3.2unpaired-image-to-image-translation

03:45

3.3cyclegan-overview

04:09

3.4cyclegan-two-gans

01:34

3.5cyclegan-cycle-consistency

06:30

3.6cyclegan-least-squares-loss

05:27

3.7cyclegan-identity-loss

03:52

3.8cyclegan-putting-it-all-together

03:00

3.9cyclegan-applications-variants

04:24

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