Câu đố về cặp song sinh Barlow và việc giảm dư thừa

Câu trả lời và giải thích xuất hiện khi bạn làm bài kiểm tra.

1. What does Barlow Twins compare during representation learning?
   • Embeddings from two augmented views of the same image
   • A labeled image and a text caption
   • A positive view against many negative examples
   • Two outputs from unrelated tasks
2. What target does the Barlow Twins objective push the cross-correlation matrix toward?
   • A matrix of all zeros
   • The identity matrix
   • A matrix of all ones
   • A diagonal matrix with random entries
3. What does a diagonal term in the objective encourage?
   • Different labels for augmented views
   • Independence from the other view
   • Similar features across the two views of the same example
   • A larger number of negative pairs
4. What does the off-diagonal penalty encourage?
   • Higher similarity between every feature pair
   • More negative image examples
   • A batch-dependent class label
   • Less redundancy among different embedding components
5. How does Barlow Twins differ from methods that require negative pairs?
   • It does not need negative examples in its objective
   • It uses more negative examples per image
   • It replaces negatives with class labels
   • It needs a separate model for every class
6. Which mechanism does Barlow Twins use to avoid constant collapsed representations?
   • An explicit set of negative labels
   • The redundancy-reduction objective on the cross-correlation matrix
   • A frozen target encoder updated by EMA
   • A supervised classification head
7. Does Barlow Twins require a predictor and stop-gradient asymmetry like BYOL?
   • Yes, both are required in the original method
   • Only when the batch contains one image
   • No; its paired networks use the correlation objective without that asymmetry
   • It uses a discriminator instead
8. What is a careful claim about Barlow Twins benchmark performance?
   • It will outperform every supervised method on all tasks
   • It proves correlation objectives are always best
   • It guarantees useful features without downstream testing
   • It performed well in the specific image evaluations reported by its authors