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Deep Learning for Computer Vision · End Term · 31 Aug 2025 · May 2025 term · Set QIA3

Question 5: Vector Quantized Variational Autoencoder (VQ-VAE) utilize…

Question 5

+2 marksOne correct option

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. A

    It performs the non-linear transformation of the input data to a higher dimensional space.

  2. B

    It regularizes the encoder by penalizing complex encodings.

  3. C

    It provides a finite set of vectors which the encoder’s outputs are mapped to, effectively quantizing the latent space.

  4. D

    It decodes the quantized vectors back into the reconstructed input space.

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Correct answer

  • C

    It provides a finite set of vectors which the encoder’s outputs are mapped to, effectively quantizing the latent space.

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Question 5 of 49 in the IIT Madras BS Deep Learning for Computer Vision (Deep Learning for Computer Vision) End Term paper sat on 31 Aug 2025, in the May 2025 term (IIT M IMPROVEMENT AN EXAM QIA3 31 Aug 2025). It carries 2 marks.

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