Deepfake Generation & Detection: Topical Research Presentation by Andrew Villa

what type of magis is this . he is writing backwards

I absolutely love this topic. The advances in human medicine could be incredible with this. A sample "input" from a bio organism...and then a model "of you're target cell types"...and then prediction on outcomes...and then further samples of "feedback agent" and then training you're human cell model. Then we introduce the GAN and think about our models accuracy. The future state possibilities of identifying interactions "trainings" with various drugs etc. This type of interaction could lead to identifying bio organisms not just humans and potential outcomes of interactions with them. Extrapolate that with humans and food allergies, diseases etc. It's mind boggling. When he is talking about CNN's and the use of alternate examples with Discriminators and Generators with Encryption my mind exploded. You could, hypothesize a Hedy Lamar like frequency agility but apply that to encryption and use an encryption agile chain. Good lord, super computationally expensive but man that would be nearly unusable from theft point of view. Would take you forever to crack that..as all the data could change from one form to another over time of transmission.

Excellent video

this is what you call a clear explanation, thanks

Bro just kept talking and said nothing

Loved it😅

Did DALL-E 2 use GAN?

Very well explained. Thanks for sharing

Good job bro, JESUS IS COMING BACK VERY SOON; WATCH AND PREPARE

Yo he writing backwards

Simply Loved it

why don't you have a link to the CNN video that he mentions?

A gan is a speedcube

loved it. simple enough to be understood yet complex enough to get the important details

if we are giving the discriminator a domain for learning shapes of flower isnt is supervised learning how it is unsupervised since we are providing a domain to learn

Great video, perfect presentation. Was this artificially generated?

For the image upscale problem, would we still feed the generator random noise or will we give it the lower res image?

what is the difference between a discriminator and a classifier? or are these synonyms. reason i am asking is: classifiers are sometimes mentioned when it comes to detection of generated content. but, if a discriminator in the endstages of many iterations is basically no better than guessing it does not seem a viable solution for this problem

Are you really writing all of this backwards?

Super- thank you :)

I don't get that the discriminator should be updated if the generator succeeds. The image was 'fake' ( i would say synthesized ) and the whole point of the game beeing to teach the generator how to synthesize image that are as far as possible close to the 'real data' dataset. There is no failure per say.
It all depends on what you means by fake:

1- Fake means even if its a realistic flower but does not belong to the 'real' dataset it a fake.
2- Fake means its not a flower ,its a car , or garbage so the discriminator is unhappy of the generator's job.

You seem to define fake as per definition 1 ; in this case , you can directly compare image pixels by pixels and calculate euclidian distance for the error to backpropagate on the generator, you don't need a neural network for the discriminator , do you?
So i think the correct definition is 2. Hence the discriminator never has to learn from the generator.
>> I know you work for IBM , so its likely that i missed a point , kindly let met know 🙂 <<
What does make sense to me is that the discriminaor is supervising the generator , with first supervised learning for the discriminator with image belonging to the class we want to synthesize ( real like you say ) and some that are undesirable ( fake , like a boat , a cupcake). Then we freeze the discriminator because it has all the knowledge we had to provide.
Second step the discriminator supervise the generator by means of the backpropagation. The discriminator acts like an erreor function which is the likelyhood of the image to belong to the desired class.

Really perfect explanation of GAN, well done!!

Didn't most everyone else think that is not what zeromsum game meant..inthoight if there is an advantage for one player that would not be a zero sum game..

Dam.... thanks for sharing it so clearly !!!

I want to generate images through GAN from MIAS dataset. Which GAN architecture is most suitable?

Just one sentence: The easiest yet more powerful explanation of GAN!

Very nice explanation! Thanks sir

Amazing explanation

He's not really left handed, you know.

I've had a few supervisors that I'm sure were fake samples.😐

Thank you very much for this video it was very helpful and comprehensive. ☺

I have two questions regarding the image generation. Maybe you can help me:) 

1.Taking your example of generating a picture of a flower; does the generator have any kind of "knowledge" of how a flower roughly looks in the beginning? Or does it randomly give a pattern of pixels to the discriminator and learns by the rejection it gets?

2. How do GANs work in the text-to-image generators? For example, I wanted to have an image of a blue banana and my GAN gets this input as a text prompt, how would Discriminator and Generator tackle this? Would the input be relevant only to the discriminator?


Thank you!

superb backwards writing

Could somebody explain to me the difference between a GAN and Zero-Shot Learning?

Is this what Nvidia is using for its new frame generation technique in the RTX 40 series? I'm just guessing before checking the internet

Very Informative video.Thanks for making it.

No it’s a cubing company

Are we just going to ignore the fact that he's writing backwards??? That thing is skill man

You made it so easy to understand. Thank you!

elegant explanation .....great job

can someone tell me wht the core idea behind DDQN and GAN is same

It is really helpful, thanks for your video

I loved the lesson.But GANs more :)

He is either a lefty that can write mirror image sentences from right to left in real time, or the video was post processed?

how do you write backwards so well lol

Very well explained😇, thank you.

I hope the host understands that he could write normally, instead of reflected, since he just needs to mirror the video in the end and everything would be correct from the viewers view.

Excellent Explanation!

good explanation

I don't think it's very nice to talk about someone else's amazing invention without mentioning their name. (Ian Goodfellow created GANs in 2014)