quiz bu lëkkale ak tegtal · Dëgër Niveau

BYOL ak Quiz buy saytu sa bopp te amul benn wuute

Dafay saytu BYOL ci net bi ak ci bànxaas yi, yokk gis-gis yi, wax luy waaja am, yeesali moyenne yuy toxu, ak jàngu representation bu amul etiket.

Yooni njiit yu jëm ciByol ak saytu sa bopp bu amul wuute
Laaj 1 de 8

What makes BYOL non-contrastive?

Xoolaatal laaj bu nekk8 laaj

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

  1. What makes BYOL non-contrastive?
    • It trains only with class labels
    • It learns without using negative image pairs
    • It does not use image augmentations
    • It compares each image with every other image
  2. What are BYOL’s two networks called?
    • Online and target networks
    • Encoder and classifier only
    • Teacher and discriminator
    • Query and key databases
  3. What does the online network learn to predict?
    • The image’s human class label
    • A negative pair’s representation
    • The target network’s raw pixel values only
    • The target network’s representation of another augmented view of the same image
  4. How are target-network weights updated?
    • By ordinary gradient descent on the same loss
    • They remain random and frozen forever
    • By a slow-moving exponential average of online weights
    • By copying the classifier head each batch
  5. Why does BYOL use an online predictor and stop-gradient on the target side?
    • They create an asymmetric learning setup that helps avoid representation collapse
    • They add negative examples
    • They turn the task into supervised classification
    • They make the target receive the same gradient
  6. What happens to two views of the same image in BYOL?
    • They are assigned different class labels
    • They are encoded in the two networks and used to align learned representations
    • They are compared only to unrelated images
    • They are discarded after augmentation
  7. Does BYOL require human class labels during its representation pretraining?
    • Yes, every view needs a class label
    • Only the target branch is labeled
    • No; it is a self-supervised image-representation method
    • It needs labels for negative pairs
  8. What is a fair interpretation of BYOL’s benchmark results?
    • They prove labels are never useful
    • They guarantee superiority on all datasets
    • They show that all non-contrastive methods work identically
    • They demonstrate performance on specified evaluations, not a guarantee for every task