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DL for CV · Generative models

10 questions · 20 marks · 2 papers

Answer key

Indian Institute of Technology Madras

BS Degree Programme · Examination & Study Paper

Deep Learning for Computer Vision

Generative models

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.

End Term · 31 Aug 2025 (FN)End Term

Q1MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following statements are false? (Select all that apply)
1. AGenerative adversarial networks (GANs) generate sharper images compared to Variational AutoEncoders (VAE)
2. BGAN is an example of an implicit density estimation model
3. CFully connected layers in mapping network of Style-GAN do not change the dimension of its input
4. DThe generator and discriminator are always trained together in a GAN
Q2MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following statements are true? (Select all possible correct options)
1. AAutoencoder are equivalent to Principal Component Analysis (PCA) provided we don’t use of non-linear activation functions
2. BWhen using global attention on temporal data, alignment weights are learnt for encoder hidden representations for all time steps
3. CPositional encoding is an important component of the transformer architecture as it conveys information about order in a given sequence
4. DIt is not possible to generate different captions for the same image that have similar meaning but different tone/style
5. EAutoencoders can not be used for data compression as its input and output dimensions are different
Q3NAT2 Marks·Write the number
Asked 2 times Key ↓
Your answer:
Q4NAT2 Marks·Write the number
Asked 2 times Key ↓
Your answer:

End Term · 31 Aug 2025 (AN)End Term

Q5MCQ2 Marks·Choose one option
Key ↓
Vector Quantized Variational Autoencoder (VQ-VAE) utilizes a discrete latent representation as opposed to continuous latent spaces used in traditional VAEs. One of the key components of a VQ- VAE is the codebook, which consists of a set of learnable vectors. What is the primary role of the codebook in VQ-VAE?
1. AIt performs the non-linear transformation of the input data to a higher dimensional space.
2. BIt regularizes the encoder by penalizing complex encodings.
3. CIt provides a finite set of vectors which the encoder’s outputs are mapped to, effectively quantizing the latent space.
4. DIt decodes the quantized vectors back into the reconstructed input space.
Q6MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following statements are true? (Select all possible correct options)
1. AVariational AutoEncoders (VAE) generate sharper images compared to Generative adversarial networks (GANs)
2. BGAN is an example of an implicit density estimation model
3. CFully connected layers in mapping network of Style-GAN change the dimension of its input
4. DIn a GAN, the generator and discriminator are trained in an alternating fashion, each using the other’s output to improve.
Q7MSQ2 Marks·Choose all correct options
Key ↓
Classifier-free guidance is a technique used in diffusion models to improve sample quality without the explicit use of a classifier. This technique involves modifying the sampling process based on a control parameter. If the control parameter, denoted as γ, is set to zero, what effect does this have on the generation process?
1. AIt fails to generate realistic samples
2. BIt removes all guidance, effectively making the process equivalent to the unconditional generation.
3. CIt maximizes the influence of the classifier, leading to highly detailed generations.
4. DIt can lead to more diverse samples compared to higher values of γ, as the generation process is less constrained by the conditional information.
Q8MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following statements are true? (Select all possible correct options)
1. AAutoencoder are equivalent to Principal Component Analysis (PCA) provided we don’t use of non-linear activation functions
2. BWhen using global attention on temporal data, alignment weights are learnt for encoder hidden representations for all time steps
3. CPositional encoding is an important component of the trans- former architecture as it conveys information about order in a given sequence
4. DIt is not possible to generate different captions for the same image that have similar meaning but different tone/style
5. EAutoencoders can not be used for data compression as its input and output dimensions are different
Q9NAT2 Marks·Write the number
Asked 2 times Key ↓
Your answer:
Q10NAT2 Marks·Write the number
Asked 2 times Key ↓
Your answer:

Official Key

Master Answer Key & Explanations

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

Reveal Key

Q1C, D

Q2A, B, C

Q345

Q43.75

Q5C

Q6B, D

Q7B, D

Q8A, B, C

Q943

Q103.75

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