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DLP · End Term · 31 Aug 2025 (Forenoon)

22 questions · 100 marks · 90 min

Answer key Take as mock

Indian Institute of Technology Madras

BS Degree Programme · Examination & Study Paper

Deep Learning Practice

End Term · 31 Aug 2025 (Forenoon)

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.
NAT: Numerical response. No negative marking.
Scientific calculator allowed for all calculations.
Official answer key is sealed at the end of this sheet.

Q1MCQ5 Marks·Choose one option
Asked 2 times Key ↓
Consider the task of applying a convolutional layer with a stride of 1 to an image of size 227 × 227 (Grayscale), while ensuring that the output feature map retains the same spatial dimensions. Which of the following code snippets correctly implements this using PyTorch?
1. A
2. B
3. C
4. D
Q2MCQ5 Marks·Choose one option
Asked 2 times Key ↓
1. AOutput size: 8 × 8; Effective receptive field: 5 × 5
2. BOutput size: 10 × 10; Effective receptive field: 5 × 5
3. COutput size: 10 × 10; Effective receptive field: 3 × 3
4. DOutput size: 12 × 12; Effective receptive field: 7 × 7
Q3MCQ5 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
Q4MCQ5 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.
Q5MCQ5 Marks·Choose one option
Asked 3 times Key ↓
1. A
2. B
3. C
4. D
Q6MCQ5 Marks·Choose one option
Key ↓
1. AA–(e), B–(c), C–(a), D–(d), E–(b)
2. BA–(e), B–(b), C–(c), D–(a), E–(d)
3. CA–(e), B–(a), C–(c), D–(d), E–(b)
4. DA–(c), B–(e), C–(a), D–(b), E–(d)
Q7MCQ5 Marks·Choose one option
Asked 2 times Key ↓
Consider the original U-Net architecture. Which of the following statements about the convolutional and pooling operations is correct?
1. AU-Net uses 5 × 5 convolutional filters and 2 × 2 max pooling filters with stride 2 in the encoder path.
2. BU-Net uses 3 × 3 convolutional filters and 2 × 2 average pooling filters with stride 1 in both encoder and decoder.
3. CU-Net uses 3 × 3 convolutional filters (with padding) and 2 × 2 max pooling filters with stride 2 to halve the spatial resolution in the encoder.
4. DU-Net uses 3 × 3 filters and 1 × 1 pooling to preserve spatial dimensions between contracting and expanding paths.
Q8MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are valid advantages of using Convolutional Neural Networks (CNNs) instead of fully connected Multilayer Perceptrons (MLPs) for image-based tasks?
1. ACNNs require significantly fewer parameters than MLPs for high-dimensional image inputs.
2. BCNNs are better at capturing spatial hierarchies due to weight sharing and local connectivity.
3. CCNNs can approximate any function with fewer layers than MLPs due to the universal approximation theorem.
4. DCNNs are translation invariant by design, making them robust to object position shifts in images.
5. ECNNs do not require any activation functions since convolutions are linear operators.
6. FCNNs are computationally faster than MLPs on image data due to sparse connections and weight sharing.
Q9MSQ5 Marks·Choose all correct options
Key ↓
In the Faster R-CNN architecture, the Region Proposal Network (RPN) plays a critical role in object detection. Which of the following statements correctly describe the role of the RPN?
1. AThe RPN generates a set of class-agnostic region proposals likely to contain objects.
2. BThe RPN performs classification and bounding box regression to output object classes and their exact locations.
3. CThe RPN uses anchor boxes of different scales and aspect ratios to slide over convolutional feature maps.
4. DThe RPN replaces traditional region proposal methods like selective search, making the pipeline faster and end-to-end trainable.
5. EThe RPN outputs fixed-size feature maps that are directly used for object classification.
6. FThe RPN filters out background regions by assigning objectness scores to anchors.
Q10MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are valid advantages of YOLO compared to Fast R-CNN or Faster R-CNN?
1. AYOLO performs object classification and localization in a single forward pass, making it faster than two-stage detectors like Fast R-CNN and Faster R-CNN.
2. BYOLO does not require region proposal generation, making the entire pipeline fully end-to-end trainable.
3. CYOLO achieves higher localization accuracy than Faster R-CNN on small objects due to its coarse grid-based prediction.
4. DYOLO treats bounding box coordinates and objectness score as regression outputs, while computing class probabilities through classification.
5. EYOLO achieves higher precision by using a large number of anchor boxes per region.
6. FYOLO treats object detection as a regression problem, which simplifies the detection pipeline and reduces computational overhead.
Q11MSQ5 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.
Q12MSQ5 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
Q13MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following statements are correct regarding Generative Adversarial Networks (GANs)?
1. AThe generator tries to minimize the probability that the discriminator correctly classifies fake samples.
2. BThe discriminator is trained to maximize the probability of correctly classifying real and generated (fake) data.
3. CThe generator directly receives labeled training data to learn the target distribution.
4. DDuring training, the generator is updated based on the discriminator’s feedback to improve sample quality.
5. EGAN training can suffer from instability due to the adversarial objective, leading to issues like mode collapse.
6. FA perfectly trained discriminator ensures faster convergence of the generator.
Q14MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
SRGAN and ESRGAN are both deep learning architectures designed for single image super- resolution. Which of the following statements correctly describe the key differences between ESRGAN and SRGAN?
1. AESRGAN replaces the standard residual blocks in SRGAN with Residual-in- Residual Dense Blocks (RRDB) for better performance.
2. BSRGAN employs Perceptual Loss, while ESRGAN removes it to focus only on pixel-wise MSE loss for training stability.
3. CESRGAN produces sharper and more realistic textures than SRGAN due to architectural and loss function improvements.
4. DBoth SRGAN and ESRGAN use a VGG-based perceptual loss computed on feature maps extracted from deeper and shallower layers, respectively, of a pretrained classifier.
5. ESRGAN is trained using only adversarial and perceptual losses, whereas ESRGAN includes a content loss computed using pixel-wise L1 loss.
Q15MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
The Multi-stage Progressive Image Restoration Network (MPRNet) is a state-of-the-art architecture for tasks like image deblurring and deraining. It follows a multi-stage design where features are progressively refined across stages. how many of the above components are explicitly part of the original MPRNet architecture?
1. Aa single layer neural network.
2. BUNet
3. CSupervised Attention Module
4. DChannel Attention Block
5. EFast RCNN block
Q16NAT5 Marks·Write the number
Asked 2 times Key ↓
Your answer:
Q17NAT5 Marks·Write the number
Asked 3 times Key ↓
Your answer:
Q18NAT5 Marks·Write the number
Asked 3 times Key ↓
Your answer:
Q19NAT5 Marks·Write the number
Asked 3 times Key ↓
Your answer:
Read the passage below, then answer Q20–Q22
Based on the above data, answer the given subquestions.
Q20NAT1.5 Marks·Write the number
Asked 4 times Key ↓
Compute the PSNR for Model 1 (in dB) (rounded to 2 decimal places) :
Your answer:
Q21NAT1.5 Marks·Write the number
Asked 4 times Key ↓
Compute the PSNR for Model 2 (in dB) (rounded to 2 decimal places):
Your answer:
Q22NAT2 Marks·Write the number
Asked 2 times Key ↓
Which model produces an image more similar to the ground truth?
Your answer:

Official Key

Master Answer Key & Explanations

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

Reveal Key

Q1D

Q2B

Q3B

Q4B

Q5A

Q6A

Q7C

Q8A, B, D, F

Q9A, C, D, F

Q10A, B, D, F

Q11A, B, D

Q12A, C, E

Q13A, B, D, E

Q14A, C, E

Q15B, C, D

Q163029908880

Q170.61 to 0.65

Q180.49 to 0.53

Q1964

Q2025

Q2125.55 to 25.59

Q222

Questions and answer keys are reproduced from the official IIT Madras BS papers. BTechi is not affiliated with IIT Madras.