Generative Adversarial Networks: A Beginner's Guide to GANs

[@Sunil-ez1hx]

Awesome video ma’am

[@CodeWithAarohi]

Thanks!

[@Syedzamangallani]

THANKYOU SO MUCH MAM FOR YOU THIS TO AND TO MUCH KIND VIDEO YOU DESERVE MILLIONS OF VIEW SUBSCRIBER

[@CodeWithAarohi]

You're very welcome! I'm glad you found it helpful.

[@arnavthakur5409]

Your way of explaination is excellent as always

[@CodeWithAarohi]

Thanks!

[@dibyajyotiacharya8916]

Great video ma'am🙌 Keep posting such informative videos

[@CodeWithAarohi]

I'm glad you found it helpful!

[@jeffg4686]

@2:45 - looks like StackGAN didn't do so good. That bird has black wings Maybe it's never seen any red birds with white wings, but that means it's not zero-shot really. not performing well as zero-shot. Just adding some few notes of possible generation flow here (generally speaking) Transposed Convolution 1 - generate visual patterns like edges or color gradients from noise seeds Transposed Convolution 2 - generate encodings of larger shapes, textures, or object parts Transposed Convolution 3 - maps add fine-grained details and realistic effects like shading and reflections. Output Layer -encode the final image structure and details in a way that combines all previous encodings into an image. something like that... I'm still learning, but I like to see it from high perspective.

[@CodeWithAarohi]

Haha, you're totally right about the bird! Thanks for pointing that out! I appreciate your breakdown of the transposed convolution layers too, it's great to see how you're thinking about the generation flow. Keep up the great work!

[@pifordtechnologiespvtltd5698]

Nice video

[@CodeWithAarohi]

Thanks!

[@worldsdata2332]

Thank You So Much Ma'am,

[@CodeWithAarohi]

You're welcome! 😊

[@HINITECH]

very help full mam

[@CodeWithAarohi]

Glad it helped!

[@jynpogger]

Thank You!!!

[@CodeWithAarohi]

You're welcome!

[@sanjuanand3056]

Superb vedeo Madam your explanation is clr and awesome

[@CodeWithAarohi]

Glad it helped!

[@atulpandey1979]

Can you please create a separate playlist for GANs

[@decode168]

Amazing 😻 😻 , I would like to request you a video related to Diffusion Models for Scene Text, please. thanks.

[@CodeWithAarohi]

Thank you! Noted

[@mohammadnuruzzaman5195]

Nice video ma’am please explain VAEs

[@CodeWithAarohi]

Noted! I will make a video on VAEs soon.

[@soravsingla6574]

Super

[@CodeWithAarohi]

Thanks

[@sanathspai3210]

Hi Arohi I have one doubt, How does discriminator works for batches of images? So if batch size is 4 then 4 real images will be compared with 4 random generated images?

[@CodeWithAarohi]

Yes, that's correct! For real images- The discriminator's predictions are compared with the valid labels (a tensor of ones, [1, 1, 1, 1] for a batch of size 4). For real images the discriminator tries to output probabilities close to 1. For fake images- The discriminator's predictions are compared with the fake labels (a tensor of zeros, [0, 0, 0, 0] for a batch of size 4).For fake images discriminator tries to output probabilities close to 0.

[@noorahmadharal]

Please share the notes. It will be very helpful. Thanks

[@ayushmathur4083]

Can you please make a video showing the python coding for GAN network, also please help me, I need to extract local features of the images to train the model, not the overall global positioning, is CNN a good option for that?

[@CodeWithAarohi]

Sure, Soon!

[@SureshYadav-xb7zh]

Ma'am please provide the ppt of it