DeOldify is a technique to colorize and restore old black and white images or even film footage. It was developed and is still getting updated by only one person, Jason Antic. It is now the state-of-the-art way to colorize black and white images. Best of all, everything is open-sourced! Watch the video References What are GANs: https://youtu.be/ZnpZsiy_p2M GitHub with full code, in-depth explanation, and Colabs: https://github.com/jantic/DeOldify Free DeepAI image colorization API using DeOldify: https://deepai.org/machine-learning-m... MyHeritage colorization tool (the best version of DeOldify available, paid): https://www.myheritage.com/incolor Follow me for more AI content: Instagram: https://www.instagram.com/whats_ai/ LinkedIn: https://www.linkedin.com/in/whats-ai/ Twitter: https://twitter.com/Whats_AI Facebook: https://www.facebook.com/whats.artifi... 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You'll learn a lot of cool stuff, I promise. 0:40 - DeOldify explanation 3:22 - How to use DeOldify Yourself 4:24 - Old Movie Examples 5:06 - Conclusion Video transcript 00:00 this ai can colorize and restore old 00:03 black and white images and even film 00:05 footage 00:06 this method is called de-aldefy and 00:09 works on pretty much 00:10 any picture if you don't believe me you 00:13 can even try it yourself for free as i 00:15 will show in the video 00:17 but first let's see how it works and 00:19 more amazing results to convince you to 00:22 try it 00:25 [Music] 00:29 this is what's ai and i share artificial 00:31 intelligence news every week 00:33 if you are new to the channel and want 00:34 to stay up to date please consider 00:36 subscribing to not miss any further news 00:40 the aldephi is a technique to colorize 00:42 and restore 00:43 old black and white images or even film 00:45 footage 00:46 it was developed and is still getting 00:48 updated by only one person 00:50 jason antique it is now the 00:53 state-of-the-art way to colorize black 00:55 and white images 00:56 and everything is open sourced but we 00:59 will get back to this in a bit 01:01 first let's see how he achieved that it 01:04 uses a new type of gun training method 01:07 called nogan that he developed himself 01:09 to solve the main problems that appeared 01:11 when training using a normal 01:13 adversarial network architecture 01:15 composed of a discriminator and a 01:17 generator 01:18 typically gun training works by both 01:21 training the discriminator and generator 01:23 at the same time 01:24 where the generator starts by being 01:26 completely random 01:27 and improves over time to fool the 01:29 discriminator 01:30 which tries to tell if the image is 01:32 generated or real 01:34 if this was just completely abstract to 01:36 you i invite you to watch the video i 01:38 made about cans 01:39 in the upper right corner right now and 01:41 linked in the description 01:43 before continuing this video his new 01:46 method 01:46 which he calls the nogan provides the 01:49 same benefits of this usual gun training 01:52 while having to spend way less time 01:54 training the gan architecture 01:56 which is typically pretty heavy in 01:58 computation time 01:59 instead he pre-trains the generator to 02:02 make it already more powerful 02:04 fast and reliable using a regular loss 02:07 function 02:08 this is done by training the generator 02:10 like a regular deep networks 02:12 architecture 02:13 such as resnet that way the model is 02:16 already pretty good at colorizing an 02:18 image 02:18 before training the complete gan 02:20 architecture 02:22 then it only needs a short amount of 02:24 this typical generator discriminator 02:26 gan training to optimize the realism of 02:29 the generated pictures 02:32 gaussian noise is also randomly applied 02:34 to images to generate 02:36 fake noise during training this is a 02:39 type of data augmentation that can be 02:41 performed on the training images to 02:43 improve the results and resistance 02:45 to noisy inputs using the same technique 02:48 as style transfer where the noise will 02:50 be the style of the image we want to 02:52 copy 02:53 and can be applied more or less to the 02:55 transformation 02:57 the whole architecture uses a basic 02:59 resnet backbone 03:00 on a u-net where the generator network 03:02 in the gun training 03:04 is the unet architecture right now 03:07 there is no complete explanation of how 03:09 this works 03:10 but the author is currently working on a 03:13 paper about 03:14 the aldephi where he will further 03:16 investigate 03:17 why and how his technique previously 03:20 found only by trials and errors 03:22 work you can find three things in the 03:25 description of the video 03:27 at first there's the github link with a 03:30 complete detailed explanation of the 03:31 technique and even google collab 03:33 tutorials 03:34 to use it yourself just look at how 03:37 simple this is 03:38 you can just run the few sections enter 03:41 the link of your image 03:43 and run it 04:04 then you can find a free api on deep ai 04:08 using dl defy where you can simply click 04:11 and try yourself 04:13 finally the third link is the most 04:15 advanced version of the aldify if you 04:17 are looking for the best results 04:20 it is on myheritage's website and is 04:23 paid to use 04:24 let's just take a minute to see how it 04:26 works on old movies before ending this 04:40 video 04:50 you better get on the job some of the 04:52 kids may be up this afternoon 04:53 oh jack we can get along without 04:55 dragging those young kids up here oh why 04:57 don't you button up your lip 04:59 you're always squawking about something 05:00 you got more static on the radio 05:07 please leave a like if you went this far 05:09 in the video 05:10 and since there are over 90 of you guys 05:13 watching that are not subscribed yet 05:15 consider subscribing to the channel to 05:17 not miss any further news clearly 05:19 explained 05:20 thank you for watching 05:23 [Music]