NPTEL Deep Learning - IIT Ropar Week 6 Assignment Answer 2023

NPTEL Deep Learning – IIT Ropar Week 6 Assignment Solutions

NPTEL Deep Learning – IIT Ropar Week 6 Assignment Answer 2023

1. What is the main purpose of a hidden layer in an under-complete autoencoder?

To increase the number of neurons in the network
To reduce the number of neurons in the network
To limit the capacity of the network
None of These

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2. Which of the following problems prevents us from using autoencoders for the task of Image compression?

Images are not allowed as input to autoencoders
Difficulty in training deep neural networks
Loss of image quality due to compression
Auto encoders are not capable of producing image output

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3. Which of the following is a potential advantage of using an overcomplete autoencoder?

Reduction of the risk of overfitting
Ability to learn more complex and nonlinear representations
Faster training time
To compress the input data

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4. What is/are the primary advantages of Autoencoders over PCA?

Autoencoders are less prone to overfitting than PCA.
Autoencoders are faster and more efficient than PCA.
Autoencoders require fewer input data than PCA.
Autoencoders can capture nonlinear relationships in the input data.

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5. Which of the following is a potential disadvantage of using autoencoders for dimensionality reduction over PCA?

Autoencoders are computationally expensive and may require more training data than PCA.
Autoencoders are bad at capturing complex relationships in data
Autoencoders may overfit the training data and generalize poorly to new data.
Autoencoders are unable to handle linear relationships between data.

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6. What is the primary objective of sparse autoencoders that distinguishes it from vanilla autoencoder?

They learn a low-dimensional representation of the input data
They minimize the reconstruction error between the input and the output
They capture only the important variations/features in the data
They maximize the mutual information between the input and the output

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7. Which of the following networks represents an autoencoder?

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8. If the dimension of the hidden layer representation is more than the dimension of the input layer, then what kind of autoencoder do we have?

Complete autoencoder
Under-complete autoencoder
Overcomplete autoencoder
Sparse autoencoder

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9. Suppose for one data point we have features x1,x2,x3,x4,x5 as −2,12,4.2,7.6,0 then, which of the following function should we use on the output layer(decoder)?