BTechibtechi.in
Loading Loading
Free IIT Madras BS notes, PYQs and mock tests at btechi.in

DLP · End Term · 31 Aug 2025 (Afternoon)

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 (Afternoon)

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 111 × 111 (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: 15 × 15; Effective receptive field: 5 × 5
2. BOutput size: 17 × 17; Effective receptive field: 5 × 5
3. COutput size: 13 × 13; Effective receptive field: 5 × 5
4. DOutput size: 15 × 15; Effective receptive field: 3 × 3
Q3MCQ5 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
Q4MCQ5 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.
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–(d), B–(e), C–(a), D–(b), E–(c)
2. BA–(d), B–(a), C–(e), D–(b), E–(c)
3. CA–(e), B–(d), C–(a), D–(c), E–(b)
4. DA–(d), B–(e), C–(b), D–(a), E–(c)
Q7MCQ5 Marks·Choose one option
Asked 2 times Key ↓
In the original U-Net architecture, which of the following statements about the decoder (expanding) path is correct?
1. AThe decoder upsamples the feature maps using 2 x 2 transposed convolutions and concatenates them with corresponding encoder features.
2. BThe decoder uses 3 x 3 max pooling to increase the spatial resolution before feature fusion.
3. CThe decoder applies 1 x 1 convolutions to increase spatial dimensions and reduce channel depth.
4. DThe decoder merges encoder and decoder features using element-wise addition instead of concatenation.
Q8MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are valid reasons why Convolutional Neural Networks (CNNs) are preferred over fully connected Multilayer Perceptrons (MLPs) for image processing tasks?
1. ACNNs reduce the number of parameters by using local connections and weight sharing.
2. BCNNs can process images of arbitrary size without any architectural changes.
3. CCNNs exploit the spatial structure of images, making them more efficient at recognizing patterns.
4. DCNNs perform better than MLPs because they always use deeper networks.
5. ECNNs maintain spatial hierarchies by stacking convolution and pooling layers.
6. FCNNs can generalize better to unseen data due to their built-in translational invariance.
Q9MSQ5 Marks·Choose all correct options
Key ↓
In the Faster R-CNN architecture, the Region of Interest (RoI) Pooling layer plays a key role in the object detection pipeline. Which of the following statements correctly describe the function and properties of RoI Pooling?
1. ARoI Pooling converts variable-sized region proposals into fixed-size feature maps for the classifier head.
2. BRoI Pooling crops the region proposals from the input image and resizes them to a fixed dimension.
3. CRoI Pooling enables batching and fully connected layers by standardizing the input dimensions.
4. DRoI Pooling uses bilinear interpolation to produce smoother feature maps for each proposal.
5. ERoI Pooling operates on convolutional feature maps, not directly on the raw image.
6. FRoI Pooling discards proposals with low objectness scores before passing them to the classifier.
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 is faster at inference time because it predicts bounding boxes and class probabilities in a single pass.
2. BYOLO is fully end-to-end trainable and does not rely on external region proposal mechanisms.
3. CYOLO achieves higher recall than Faster R-CNN on all types of objects, especially small and overlapping ones.
4. DYOLO formulates detection as a combination of regression (for bounding box coordinates and objectness score) and classification (for discrete object categories).
5. EYOLO uses multi-scale feature fusion to improve detection of small objects.
6. FYOLO’s architecture allows it to be deployed more easily on real-time and edge devices due to its compactness.
Q11MSQ5 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.
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 the training dynamics and properties of Generative Adversarial Networks (GANs)?
1. AThe generator’s objective is to produce data that the discriminator cannot distinguish from real data.
2. BThe discriminator is trained to minimize the distance between the generated data and the real data distributions.
3. CGAN training is a min-max game where the generator and discriminator have opposing goals.
4. DMode collapse refers to the generator producing diverse outputs that cover multiple modes of the data distribution.
5. EA balance between generator and discriminator learning rates is important to ensure stable training.
6. FConditional GANs extend standard GANs by incorporating label information into both the generator and discriminator.
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 about the differences between ESRGAN and SRGAN are NOT correct?
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 layers 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 designed to progressively restore degraded images across multiple stages. It integrates several architectural innovations to enhance image restoration performance. How many of the above components are explicitly part of the original MPRNet architecture?
1. AUNet-style encoder-decoder at each stage
2. BResidual Channel Attention Network (RCAN)
3. CSupervised Attention Module (SAM)
4. DSkip connections across stages
5. ENon-local Attention Module
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

Q1A

Q2A

Q3A

Q4C

Q5A

Q6A

Q7A

Q8A, C, E, F

Q9A, C, E

Q10A, B, D, F

Q11C, E, F

Q12A, C, E

Q13A, C, E, F

Q14B, D

Q15A, C, D

Q16394264576

Q170.61 to 0.65

Q180.3 to 0.34

Q1925

Q2026.33 to 26.37

Q2124.86 to 24.9

Q221

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