Quiz Space

Deep Learning for Computer Vision · Quiz 2 · 12 Apr 2026 · January 2026 term

Question 7: When fine-tuning a model, what is a common way to prevent…

Question 7

+1 markOne correct option

When fine-tuning a model, what is a common way to prevent the new training from "overwriting" or destroying the useful features already learned during pre-training?

  1. A

    Applying a high dropout rate exclusively to the newly initialized classification head

  2. B

    Use differential learning rates (small LR for the pre-trained backbone, larger LR for the new head)

  3. C

    Re-initializing the weights of the last convolutional block before training

  4. D

    Freezing the classification head and only fine-tuning the backbone features

Show answer

Correct answer

  • B

    Use differential learning rates (small LR for the pre-trained backbone, larger LR for the new head)

See the whole paperTake it as a mock test

Question 7 of 23 in the IIT Madras BS Deep Learning for Computer Vision (Deep Learning for Computer Vision) Quiz 2 paper sat on 12 Apr 2026, in the January 2026 term (Deep Learning For Computer Vision 07 Apr 26). It carries 1 mark.

More questions from this paper

  1. Q1Why do very deep plain CNNs (without skip connections) sometimes show higher training error than shallower CNNs?
  2. Q2In a standard ResNet-50 bottleneck block, the three convolutions are typically:
  3. Q3In Inception/GoogLeNet modules, the main purpose of 1×1 convolutions is to:
  4. Q4MobileNetV1 reduces computation primarily by using:
  5. Q5EfficientNet’s key scaling idea is:
  6. Q6You have a pretrained CNN backbone and only 500 labeled images for a new task. A strong first baseline is:
  7. Q8Which method produces a heatmap by weighting convolutional feature maps using gradients of a target class score?
  8. Q9Which detector is a classic two-stage detector with an RPN + ROI feature extraction?
  9. Q10Which statement is most accurate?
  10. Q11RetinaNet is notable primarily because it introduced:
  11. Q12A model that predicts a class label for every pixel without separating object instances is:
  12. Q13Vanishing gradients in vanilla RNNs are largely caused by:
  13. Q14Which statement is true?
  14. Q15Which statements can be true in practice when fine-tuning with small batch sizes?
  15. Q16Which statements are true?
  16. Q17Which statements are true ?
  17. Q18Input feature map: 14×14×128, output: 14×14×256, kernel: 3×3, stride 1, same padding, ignore bias. Compute number of pa…
  18. Q19Using the same setup (Input feature map: 14×14×128, output: 14×14×256, kernel: 3×3, stride 1, same padding, ignore bias…
  19. Q20A ResNet bottleneck block takes 256 channels in and uses: 1×1 conv to 256 channels → 3×3 conv at 256 → 1×1 conv to 1024…
  20. Q21A detector uses three pyramid levels: P3 = 80×80, P4 = 40×40, P5 = 20×20. At each location, it uses 9 anchors (3 scales…
  21. Q22Box A: top-left (2,2), bottom-right (10,12) Box B: top-left (6,5), bottom-right (14,15) Compute IoU rounded to 3 decima…
  22. Q23Let query q = [1,2]T and keys: k1 = [1,0]T, k2 = [0,1]T, k3 = [1,1]T. Scores si = qT ki. Use softmax over scores. Use e…