VQGAN ak Quiz buy boole nataali Codebook

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Lan la VQGAN di jëfandikoo ngir wane nataal ni ay jeton yu wuute?
   • Téere kode buñ jàngee ci vecteur yiy génne encoder
   • Limu tur fichier yi
   • Pixel RGB yu ñor yi kese
   • Benn nimero ci nataal bu nekk
2. What did the original VQGAN paper report gaining from adversarial and perceptual training?
   • To guarantee every VQVAE reconstruction is blurry
   • To preserve perceptual quality at increased compression in the original paper’s evaluated settings
   • To eliminate the need for a learned codebook
   • To make all images use the same token grid regardless of dataset
3. Bu benn nataal nekkee ay jeton yu toppalante, ban model mooy defar nataal yu bees?
   • këyitu xayma
   • kalkulatër
   • Transformatër buy dellu ginaaw buy wax luy waaja am ci ay jeton yu melni kàddu
   • Dawalkatu imprimante
4. Ban pexe mooy may gradient yi ñu jaar ci jéego kantite bu mënul wuute?
   • Dàddu
   • Normalisasioŋ ci lots
   • Dagg ci anam wu bari
   • Xaymakat bi jub
5. Ban art IA bu siiw la VQGAN jàppale ci defar ci 2021?
   • VQGAN+CLIP art buy defar
   • Impression 3D
   • Klonaas bu baat
   • Otomatisation këyitu xayma
6. Lan mooy jafe-jafe biñ gëna xam ci téere kode yu nuroo ak VQGAN?
   • Mënuñu denc benn vecteur
   • Codebook dafa daanu, fu kode yu bari duñu jëfandikoo
   • Duñu soxla benn tàggat
   • audio rek lañuy liggéey
7. Why does VQGAN use a compact latent token grid for high-resolution images?
   • It removes the need for an image decoder
   • It makes every codebook entry a full-resolution image
   • It shortens the transformer sequence so modeling image composition is more tractable
   • It converts the transformer into a pixel-level convolution
8. Which loss does the VQGAN guide pair with adversarial training to improve reconstruction quality?
   • A language-model next-token loss
   • A contrastive audio-text loss
   • A reinforcement-learning reward from human rankings
   • Ñàkk gis-gis