Variational Autoencoders QUIZ (MCQ QUESTIONS AND ANSWERS)

Encoder

Decoder

Latent space

Reconstruction loss

Data augmentation

Adversarial training

Kullback-Leibler (KL) Divergence

Batch Normalization

Data augmentation

Adversarial training

Kullback-Leibler (KL) Divergence

Batch Normalization

Limited scalability to large datasets

High computational cost

Difficulty in training stable models

Inability to generate high-resolution images

Mean Squared Error (MSE)

Cross-Entropy Loss

Kullback-Leibler (KL) Divergence

Cosine Similarity

Ability to generate new data samples

Ability to classify data samples

Ability to handle missing data

Ability to leverage both labeled and unlabeled data

Input data

Latent space representations

Decoder weights

Encoder weights

Data augmentation

Masked reconstruction

Kullback-Leibler (KL) Divergence

Feature matching

Dropout

Batch Normalization

ReLU activation function

Reparameterization trick

Ability to generate new data samples

Ability to classify data samples

Ability to handle missing data

Ability to preprocess data

To minimize the reconstruction loss

To maximize the reconstruction loss

To minimize the KL divergence

To maximize the KL divergence

Data augmentation

Adversarial training

Kullback-Leibler (KL) Divergence

Batch Normalization

Difficulty in training stable models

Inability to generate diverse samples

Higher computational cost

Limited scalability to large datasets

Conditional Variational Autoencoders (CVAEs)

Deep Variational Autoencoders (DVAEs)

Variational Recurrent Autoencoders (VRAEs)

Variational Convolutional Autoencoders (VCAEs)

To generate new data samples

To evaluate the authenticity of data samples

To compute the reconstruction loss

To introduce randomness into the latent space

Limited scalability to large datasets

High computational cost

Difficulty in training stable models

Inability to generate high-resolution images

Mean Squared Error (MSE)

Cross-Entropy Loss

Kullback-Leibler (KL) Divergence

Cosine Similarity

By memorizing training data

By sampling from the learned latent space distribution

By interpolating between input data samples

By using pre-defined templates

VAEs are faster to train

VAEs can generate new data samples

VAEs have lower computational cost

VAEs have higher reconstruction accuracy

Stochastic Gradient Descent (SGD)

Adam

RMSprop

AdaGrad

To minimize the reconstruction loss

To maximize the reconstruction loss

To minimize the KL divergence

To maximize the KL divergence

To minimize the reconstruction loss

To maximize the reconstruction loss

To minimize the KL divergence

To maximize the KL divergence

Mean Squared Error (MSE)

Cross-Entropy Loss

Kullback-Leibler (KL) Divergence

Cosine Similarity

Mean Squared Error (MSE)

Cross-Entropy Loss

Kullback-Leibler (KL) Divergence

Cosine Similarity

Uniform distribution

Gaussian distribution

Poisson distribution

Exponential distribution

Image classification

Image generation

Image segmentation

Image compression

To generate fake data samples

To evaluate the authenticity of data samples

To classify data samples

To reconstruct input data from latent space representations

To generate fake data samples

To evaluate the authenticity of data samples

To classify data samples

To map input data to a latent space distribution

Encoder and Decoder

Generator and Discriminator

Classifier and Regressor

Reinforcement Learning Agent and Environment

Andrew Ng

Geoffrey Hinton

Ian Goodfellow

Diederik P. Kingma and Max Welling