NPTEL Deep Learning – IIT Ropar Week 6 Assignment Asnwers 2024
1. Which of the following networks represents an autoencoder?
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2. If an under-complete autoencoder has an input layer with a dimension of 7, what could be the possible dimension of the hidden layer?
- 6
- 8
- 0
- 7
- 2
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3. What type of autoencoder is it when the hidden layer’s dimensionality is less than that of the input layer?
- Under-complete autoencoder
- Complete autoencoder
- Overcomplete autoencoder
- Sparse autoencoder
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4. Which of the following statements about regularization in autoencoders is always true?
- Regularisation reduces the search space of weights for the network.
- Regularisation helps to reduce the overfitting in overcomplete autoencoders.
- Regularisation shrinks the size of weight vectors learned.
- All of these.
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5. We are using the following autoencoder with linear encoder and linear decoder. The eigenvectors associated with the covariance matrix of our data X is (V1,V2,V3,V4,V5). What are the representations most likely to be learned by our hidden layer H? (Eigenvectors are written in decreasing order to the eigenvalues associated with them)
- V1,V2
- V4,V5
- V1,V3
- V1,V2,V3,V4,V5
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6. What is the purpose of a decoder in an autoencoder?
- To reconstruct the input data
- To generate new data
- To compress the input data
- To extract features from the input data
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7. What are the advantages of using a denoising autoencoder?
- Robustness to noisy input data
- Reduction of the risk of overfitting
- Faster training time
- It promotes sparsity in the hidden layer
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8. We are given an autoencoder A. The average activation value of neurons in this network is 0.06. The given autoencoder is:
- Contractive autoencoder
- Overcomplete neural network
- Sparse autoencoder
- Denoising autoencoder
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9. What are the possible applications of autoencoders?
- Data Compression
- Extraction of important features
- Reducing noise
- All of these
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10. 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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