Video: https://youtube.com/live/EUy3JqHVQTU?feature=share
Jamboard: https://jamboard.google.com/d/1Ihbq4z-TQWoHH_7spW9Q9fJt0z2tVfU-VFAPtQ2MMrI/edit?usp=sharing
Paper: https://arxiv.org/pdf/1703.10593.pdf
Implement CycleGAN based on instructions in 19.1
Template: http://share.yellowrobot.xyz/quick/2023-5-31-F72F1C44-8785-4680-9956-E7E34B00CC5E.zip
Iesniegt ekrānšāviņus ar labākajiem rezultātiem un programmas pirmkodu.
Implementēt Ģeneratora arhitektūru Pix2Pix, izmantojot pētījumu: https://arxiv.org/abs/1611.07004
Implementēt Summer2Winter Yosemite datu kopu https://www.kaggle.com/datasets/balraj98/summer2winter-yosemite
Iesniegt ekrānšāviņus ar labākajiem rezultātiem un programmas pirmkodu.
Jamboard iedotas tiesības: vecins.valters@gmail.com
Video RTMP key: ea11-mrgb-4jg2-4ajc-d4hr
Kods iepušots GIT
Video: https://youtu.be/9iEaQMn_5fc
Jamboard: https://jamboard.google.com/d/1t4I-Ow2nPsjABaiD2Qd8z_auo3bRkGZTFX5f3nmXSqI/edit?usp=sharing
Būtu labi pārtaisīt uz horse2zebra dataset (Aigai bija labi apmācīts, Reinis arī pārziana)
Hoses to zebras source code:
https://www.kaggle.com/code/balraj98/cyclegan-translating-horses-zebras-pytorch
Good material: https://towardsdatascience.com/cycle-gan-with-pytorch-ebe5db947a99
https://blog.jaysinha.me/train-your-first-cyclegan-for-image-to-image-translation/
nn.ReflectionPad2d(3),
Attīstība:
DTN-GAN
⚠️ Pix2Pix, PatchGAN => UNet architecture piespiež saglabāt aptuvenas features (Parasts GAN ar UNet prior)
⚠️ “PatchGAN” classifier, which only penalizes struc- ture at the scale of image patches
Markov random field -> independence separated by patch diameter
LSGAN - https://arxiv.org/pdf/1611.04076v3.pdf
Alternative to WGAN
CycleGAN
RecycleGAN - Temporal Information in Loss
BicycleGAN - VAE tipa GAN - nav saistīts ar RecycleGAN un CycleGAN
Better CycleGAN
Same discriminator 3 classes! https://ssnl.github.io/better_cycles/report.pdf
LSGAN
Pix2Pix - PatchGAN Discriminator (pa labi full Image GAN vs PatchGAN)
Pix2Pix infilling task
Pix2Pix GAN -> UNet
RecycleGAN paper: CycleGAN mode collapse - pa vidu obama - mainās viens pikselis, kurš iekodē informāciju.
we get better outputs with our approach combining the spatial and temporal constraints.
BicycleGAN: Z injection