Autoencoders

What is the primary objective of an Autoencoder?

• Classification

• Clustering

• Data Reconstruction

• Dimensionality Increse

Which of the following is NOT a component of an Autoencoder?

• Encoder
• Decoder
• Latent Space
• Optimizer

What does the latent space in an Autoencoder represent?

• An uncompressed version of the input data.
• A random selection of the input data.
• A compressed representation of the input data.
• A detailed explanation of the input data.

Which of the following techniques is commonly used to train an Autoencoder?

• Semi-supervised Learning

• Supervised Learning

• Reinforcement Learning

• Unsupervised Learning

What is the role of the decoder in an Autoencoder?

• To reconstruct the input data from the latent space.

• To compress the input data.

• To classify the input data.

• To visualize the input data.

Which loss function is commonly used in Autoencoders for reconstruction?

• Mean Squared Error (MSE)
• Categorical Cross-Entropy
• Hinge Loss
• Binary Cross-Entropy

What is a common application of Autoencoders?

• Data Augumentation

• Image Denoising

• Sentiment Analysis

• Text Classification

What is a Denoising Autoencoder trained to do?

• Reconstruct clean data from noisy input

• Reconstruct the noise

• Encode noisy data

• Maximize reconstruction error

What distinguishes a Variational Autoencoder (VAE) from a standard Autoencoder?

• It has no decoder

• It uses probabilistic latent variables

• It skips the encoding process

• It does not use a loss function

In a Variational Autoencoder, what distribution is typically assumed for the latent variables?

• Exponential distribution

• Bernoulli distribution

• Uniform distribution

• Gaussian distribution