Virtual Labs

Autoencoders for Representation Learning

1. What is the primary objective of an autoencoder?

a: To classify images into different categories Explanation

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b: To learn a compressed representation by reconstructing input data Explanation

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c: To predict future values in a time series Explanation

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d: To generate new images from random noise Explanation

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2. Which component of an autoencoder compresses the input into a lower-dimensional representation?

a: Decoder Explanation

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b: Classifier Explanation

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c: Encoder Explanation

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d: Pooling layer Explanation

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3. What is the bottleneck layer in an autoencoder?

a: The layer with the smallest dimension that holds the compressed representation Explanation

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b: The input layer with maximum dimensions Explanation

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c: The final output layer Explanation

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d: A layer that prevents overfitting Explanation

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4. In a denoising autoencoder, what is used as input during training?

a: Clean images only Explanation

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b: Noisy or corrupted images Explanation

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c: Random noise vectors Explanation

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d: Latent representations Explanation

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5. Autoencoders are increasingly described in modern deep learning literature as which type of learning, since the supervision signal is derived from the input itself?

a: Supervised learning Explanation

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b: Reinforcement learning Explanation

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c: Self-Supervised Explanation

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d: Semi-supervised learning Explanation

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6. Which loss function measures the pixel-wise squared difference between the input and the reconstruction?

a: Cross-entropy loss Explanation

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b: Hinge loss Explanation

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c: Kullback-Leibler divergence only Explanation

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d: Mean squared error or reconstruction loss Explanation

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7. What is the size of each image in the Fashion-MNIST dataset?

a: 32×32 pixels Explanation

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b: 28×28 pixels Explanation

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c: 64×64 pixels Explanation

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d: 224×224 pixels Explanation

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8. What advantage does a 2-D latent space provide in autoencoders?

a: Faster training speed Explanation

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b: Better reconstruction quality always Explanation

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c: Easy visualisation of learned representations Explanation

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d: Elimination of all reconstruction errors Explanation

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9. What happens if the bottleneck dimension is too small in an autoencoder?

a: The model may fail to capture important features, leading to poor reconstruction Explanation

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b: Training becomes faster Explanation

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c: The model will always overfit Explanation

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d: Latent space becomes easier to interpret Explanation

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10. What is the main advantage of using autoencoders over supervised learning methods for feature extraction?

a: Autoencoders always achieve 100% accuracy Explanation

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b: Autoencoders do not require labelled data and can learn representations from raw data alone Explanation

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c: Autoencoders can only work with text data Explanation

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d: Autoencoders eliminate the need for training Explanation

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