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DLP · CNN architectures & transfer learning

14 questions · 70 marks · 3 papers

Answer key

Indian Institute of Technology Madras

BS Degree Programme · Examination & Study Paper

Deep Learning Practice

CNN architectures & transfer learning

Official Paper

Examination Instructions & Guidelines

Solve on notebook/paper first before checking solutions.
MCQ: Exactly 1 option is correct. Negative marking applies.
MSQ: One or more options correct. Select all to score.
Scientific calculator allowed for all calculations.
Official answer key is sealed at the end of this sheet.

End Term · 31 Aug 2025 (FN)End Term

Q1MCQ5 Marks·Choose one option
Asked 2 times Key ↓
1. AAlexNet < InceptionNet-v3 < VGG-19 < ResNet-50
2. BAlexNet < VGG-19 < Inception-v3 < ResNet-50
3. CInception-v3 < AlexNet < VGG-19 < ResNet-50
4. DAlexNet < Inception-v3 < ResNet-50 < VGG-19
Q2MCQ5 Marks·Choose one option
Asked 2 times Key ↓
1. AResNet has too many parameters for this simple task.
2. BResidual connections are only useful when training very deep networks, and this task does not require such depth.
3. CResNet architectures perform poorly on low-resolution inputs due to their reliance on fixed feature scales.
4. DInceptionNet would have been better because it uses multiple receptive fields in parallel.
Q3MCQ5 Marks·Choose one option
Asked 3 times Key ↓
1. A
2. B
3. C
4. D
Q4MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are advantages of ResNet over VGG-19?
1. AResNet requires fewer parameters than VGG-19 while achieving higher accuracy.
2. BResNet’s residual connections help mitigate vanishing gradients in very deep networks.
3. COn the CIFAR-10 dataset, ResNet achieves higher training and validation accuracy using significantly fewer convolutional layers than VGG-19.
4. DResNet models can be trained with depths exceeding 100 layers without suffering from degradation problems.
5. EResNet blocks require more memory and training time due to the skip connections compared to VGG-19.
6. FVGG-19 is easier to deploy on edge devices because of its lightweight residual architecture.
Q5MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following code snippets can be used for data augmentation during training in PyTorch?
1. A
2. B
3. C
4. D
5. E

End Term · 31 Aug 2025 (AN)End Term

Q6MCQ5 Marks·Choose one option
Asked 2 times Key ↓
1. AResNet-50 > Inception-v3 > VGG-19 > AlexNet
2. BResNet-50 > VGG-19 > Inception-v3 > AlexNet
3. CInception-v3 > ResNet-50 > VGG-19 > AlexNet
4. DVGG-19 > ResNet-50 > Inception-v3 > AlexNet
Q7MCQ5 Marks·Choose one option
Asked 2 times Key ↓
Consider the following PyTorch CNN model used for a classification task on a small dataset of 32 x 32 RGB images:
1. AVGG-19 uses too many skip connections, which interfere with shallow patterns.
2. BThe original model had batch normalization, and VGG-19 lacks it by default.
3. CThe dataset is too small and simple for a deep architecture like VGG-19 to show its advantages, and may even overfit.
4. DVGG-19 performs better only on grayscale images, not RGB images.
Q8MCQ5 Marks·Choose one option
Asked 3 times Key ↓
1. A
2. B
3. C
4. D
Q9MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are NOT advantages of AlexNet over InceptionNet?
1. AAlexNet is simpler and easier to implement compared to the complex multi- branch design of InceptionNet.
2. BAlexNet requires fewer parameters than InceptionNet due to its shallow architecture.
3. CAlexNet has better accuracy on ImageNet than InceptionNet due to its early adoption of ReLU and dropout.
4. DAlexNet is faster to train on small datasets because of its shallow depth and simpler structure.
5. EInceptionNet extracts multi-scale features using filters of different sizes in parallel, which improves accuracy.
6. FAlexNet lacks 1 x 1 convolutions, which are used in InceptionNet for dimensionality reduction and increased efficiency.
Q10MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following code snippets can be used for data augmentation during training in PyTorch?
1. A
2. B
3. C
4. D
5. E

End Term · 13 Apr 2025 (AN)End Term

Q11MCQ5 Marks·Choose one option
Asked 3 times Key ↓
Consider the following code snippet for modifying an AlexNet architecture to adapt it for a custom classification task with 10 output classes. Fill in the blank portion with the most appropriate code snippet. Which of the following code snippets correctly fills the blank portion to modify the AlexNet classifier while preserving the pretrained feature extraction layers?
1. A
2. B
3. C
4. D
Q12MCQ5 Marks·Choose one option
Key ↓
The following code snippets attempt to implement an Inception module in PyTorch. Identify the correct snippet(s):
1. A
2. B
3. C
4. D
Q13MCQ5 Marks·Choose one option
Key ↓
In VGGNet, which part of the architecture contributes the most to memory utilization during training, and which part contains the **highest number of parameters?
1. AThe early convolutional layers have the highest memory utilization, while the fully connected layers** have the highest number of parameters.
2. BThe fully connected layers have the highest memory utilization, while the convolutional layers have the highest number of parameters.
3. CThe deeper convolutional layers have the highest memory utilization, while the **pooling layers** have the highest number of parameters.
4. DThe batch normalization layers have the highest memory utilization, while the skip connections have the highest number of parameters.
Q14MSQ5 Marks·Choose all correct options
Key ↓
Consider the following PyTorch code snippet for a custom ResNet block. Which of the following options correctly fill in the missing code snippet?
1. A
2. B
3. C
4. D
5. E

Official Key

Master Answer Key & Explanations

Official answer keys from IIT Madras BS past papers. Click any question to review.

Reveal Key

Q1B

Q2B

Q3A

Q4A, B, D

Q5A, C, E

Q6A

Q7C

Q8A

Q9C, E, F

Q10A, C, E

Q11A

Q12C

Q13A

Q14B, C

Questions and answer keys are reproduced from the official IIT Madras BS papers. Topics and repeated question types are BTechi’s own grouping. BTechi is not affiliated with IIT Madras.