Guide-yakabatana mibvunzo · Zvakaoma Nhanho

DINO Self-Distillation Quiz

Ongorora zvigadziriso zvevadzidzisi veDINO, kusimba kwechirimwa, kudzivirira kudonha, uye nzira yakasarudzika-data inotsigira zvinofambiswa zveDINOv2.

Nzira dzekutungamira dzinoenderanaDino Self Distillation
Mubvunzo 1 ye 8

Ko 'NO' muDINO inomirirei?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ko 'NO' muDINO inomirirei?
    • Hapana mavara
    • Hapana optimizer
    • Neural output
    • Normalized chinangwa
  2. Marema ematicha network anogadziridzwa sei muDINO?
    • Nekudzika kwakananga gradient pakurasikirwa
    • Randomly nhanho imwe neimwe
    • Seavhareji inofamba yeavhareji yezviremu zvemudzidzi
    • Iwo akaomeswa nechando kubva kune yakadzidziswa modhi
  3. Nderipi dambudziko rinodzivirirwa nekuisa pakati nekurodza kwezvinobuda nemudzidzisi?
    • Kupisa kweGPU
    • Modhi inodonha kusvika painobuda nguva dzose
    • Kutakura data zvishoma
    • Kukanganiswa kwemavara
  4. Muchirongwa cheDINO chezvirimwa zvakawanda, maonero api anogashirwa nemudzidzisi?
    • Chete zvirimwa zvidiki zvemuno
    • Random ruzha zvigamba
    • Grayscale shanduro chete
    • Zvibereko zvepasi rose chete
  5. What emergent property did the original DINO study observe in vision-transformer attention maps?
    • Every map reconstructs an exact depth measurement
    • Some maps highlight object structure without segmentation-label training
    • Attention is trained directly on human-drawn masks
    • All heads produce perfect object boundaries on every image
  6. How did DINOv2 extend the visual-feature approach of the original DINO?
    • It scaled self-supervised training with curated data for transferable visual features
    • It replaced visual inputs with text-only training
    • It required segmentation labels for all pretraining images
    • It removed the student-teacher learning approach entirely
  7. In DINO training, which network receives gradients from the student-teacher cross-entropy objective?
    • Only the teacher, while the student stays frozen
    • Both networks through the same backpropagation update
    • Neither network because the outputs are fixed labels
    • The student, while teacher parameters follow a moving average
  8. What does using DINOv2 as a frozen backbone mean for a downstream task?
    • It must be retrained from scratch for every input image
    • No task-specific output component can be attached
    • Its pretrained feature-extractor weights remain fixed while task-specific components use its features
    • The pretrained features can only reproduce the original training images