SPADE Semantic Image Synthesis Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndeipi yekuisa iyo SPADE inoshandisa kugadzira mufananidzo?
   • Mepu ye semantic segmentation ichinyora dunhu rega rega
   • Tsanangudzo inotaurwa
   • Mufananidzo mumwe chete
   • A 3D point gore
2. Nderipi dambudziko nekutanga normalization rakagadziriswa neSPADE?
   • Yakaita kuti mifananidzo ive nemavara zvakanyanya
   • Normalization layers yakabvisa ruzivo rwemarongerwo
   • Yakashandisa ndangariro shoma
   • Yakatadza kubata mifananidzo yemavara
3. MuSPADE, iyo yakajairwa chiyero uye shanduko paramita ndeiyi:
   • Random ruzha
   • Tsika imwechete yakagovaniswa pamufananidzo wese
   • Spatially-inosiyana matensor akaverengerwa kubva kune segmentation mask
   • Fixed constants
4. Ndeipi yakakurumbira inopindirana app yakavakirwa paSPADE?
   • Photoshop
   • Google Shandura
   • Midjourney v1
   • GauGAN (NVIDIA Canvas)
5. Ko 'SP' muSPADE inomirirei?
   • Spatially-Adaptive
   • Super Pixel
   • Spectral Pooling
   • Sparse Projection
6. Kubatsira kunoshanda kweSPADE inodzoreka marongero ndeiyi:
   • Improving the input map’s pixel resolution without image synthesis
   • Controlling where labeled scene elements appear in the generated image
   • Translating every label into natural-language captions
   • Compressing generated scenes into smaller files
7. Why can SPADE-generated images be useful when training semantic-segmentation systems?
   • The generated images contain only unlabeled audio
   • The system predicts a transcript for every pixel
   • The input layout already supplies pixel labels for the generated scene
   • The layout removes all semantic categories
8. What can happen when a designer changes a region label in a SPADE input map?
   • It changes the audio sample rate
   • It retrains the generator from scratch
   • It removes the labels from all other regions
   • The generator can synthesize the scene with the revised semantic region