I'm writing a research paper on GAN usage and I would love to be able to cite you. You have compiled days worth of information into a 5 minute video. Simply phenomenal.
This is an amazing video. I feel like on youtube, the large majority of content about GANs are simple explanations about the adversarial idea of the GAN, but with little information about the more complicated derivations. Your video is extremely high quality and explains a more advanced topic than most are willing to cover on the site. Thank you!
Great Job. I would like to ask few questions from you about using MONAI for unpair medical images. Since I have few data to work with and I am new to Machine learning . I intend to downsample the few images (about 500) I have and used the two scenario (Gen and Dis) for the two data sets to be trained. Can this approach work ? Which should I place at the Generative model and Discriminator?
hey man, im doing a different implementation because i use pytorch. after roughly 30 epochs my generated images dont look like theyre going some where. zebras still look like zebras and horses like horses. after how many epochs did u see an improvement in this regard? thx alot !
How to evaluate the generated image is similar to the target one, We don't have the paired one right, So how we can ensure the synthesized image is similar to the original one.
I'm writing a research paper on GAN usage and I would love to be able to cite you.
You have compiled days worth of information into a 5 minute video. Simply phenomenal.
you writing a review don't you not a paper if so good luck I have papers on gans if you like to read them.
very clear. thanks. especially the cylcle picture at the end. clears up everything.
This is an amazing video. I feel like on youtube, the large majority of content about GANs are simple explanations about the adversarial idea of the GAN, but with little information about the more complicated derivations. Your video is extremely high quality and explains a more advanced topic than most are willing to cover on the site. Thank you!
Awesome job, Matt! Great explanation!
great explanation. stumbled on this via search... thank you for taking the time to explain it so well
Great explanation!!
How can I get these presentation
Thanks a lot for this !
Great Job.
I would like to ask few questions from you about using MONAI for unpair medical images.
Since I have few data to work with and I am new to Machine learning . I intend to downsample the few images (about 500) I have and used the two scenario (Gen and Dis) for the two data sets to be trained. Can this approach work ? Which should I place at the Generative model and Discriminator?
Very good explanation. Thank you very much
Thanks, can I see the implementation?
I absolutely loved your video, thank you so much for helping me understand cycle gan. Subscribed :).
Great. You explained the essence! Thank you
Great explanation! Subscribed!
amazing content. It helped me a lot
hey man, im doing a different implementation because i use pytorch. after roughly 30 epochs my generated images dont look like theyre going some where. zebras still look like zebras and horses like horses. after how many epochs did u see an improvement in this regard? thx alot !
You nailed it!
This was great, thanks!
Wow. Well done
How to evaluate the generated image is similar to the target one, We don't have the paired one right, So how we can ensure the synthesized image is similar to the original one.
by calculating distance between the output of second generator and the input image
Thanks it helped me a lot
Cool!
Thanks
chris griffin
that music is highly irritating :(
LUN KHAAAAA
GHALAT BTA RHA HAI TU