Maswali Halisi ya Urejesho wa Kitendo wa ESRGAN

Majibu na maelezo huonekana unapojibu maswali.

1. Je, ni udhaifu gani mkuu wa ESRGAN ya awali ambayo Real-ESRGAN ilishughulikia?
   • Ilifunzwa tu juu ya upunguzaji safi wa bicubic
   • Ilifanya kazi tu kwenye picha za kijivu
   • Haikuweza kukimbia kwenye GPU
   • Ilihitaji picha halisi zilizooanishwa
2. Je, ni mfano gani wa 'uharibifu wa hali ya juu' wa Real-ESRGAN?
   • Jenereta ya azimio la juu zaidi
   • Msururu wa uharibifu wa nasibu nyingi unatumika kwa mfuatano
   • Utaratibu mpya wa umakini
   • Mbaguzi mkubwa zaidi
3. Je, Real-ESRGAN hutumia usanifu gani wa kibaguzi?
   • Kiainishi cha mstari mmoja
   • Mtandao unaorudiwa
   • U-Net iliyo na urekebishaji wa spectral
   • VGG iliyohifadhiwa
4. Je, vichujio vya 'sinc' katika Real-ESRGAN huzalisha kisanii gani?
   • Ufungaji wa rangi
   • Mwako wa lenzi
   • Ukosefu wa kromatiki
   • Kupigia na overshoot
5. Jenereta ya Real-ESRGAN huhifadhi uti wa mgongo gani kutoka ESRGAN?
   • RRDB inazuia
   • Kisimbaji cha transfoma
   • U-Net ya kueneza
   • Mkusanyiko wa LSTM
6. In GFPGAN’s documented inference command, which background upsampler is set by default?
   • Halisi-ESRGAN
   • ESRGAN only
   • A fixed bicubic resize
   • No background upsampler is supported
7. What kind of images is Real-ESRGAN’s anime checkpoint intended to restore?
   • Only medical CT scans
   • Only satellite photographs
   • Anime illustrations
   • Only grayscale handwritten documents
8. Why can tiled inference help when restoring a very large image?
   • It guarantees that every tile contains the same details
   • It replaces super-resolution with image compression
   • It prevents the model from seeing any neighboring pixels
   • It processes smaller patches to reduce peak memory use