Autoencoders QUIZ (MCQ QUESTIONS AND ANSWERS)

Encoder and decoder

Encoder only

Decoder only

None of the layers

To make the training process faster

To make the data more difficult to reconstruct

To reduce the model's capacity

To improve the model's ability to handle noisy input data

Natural language translation

Image classification

Anomaly detection

Speech synthesis

ReLU (Rectified Linear Unit)

Sigmoid

Tanh (Hyperbolic Tangent)

Softmax

To add noise to the latent space

To introduce randomness into the decoder

To make the latent space continuous and differentiable

To reduce the dimensionality of the latent space

To increase training speed

To reduce model complexity

To encourage overfitting

To maximize reconstruction accuracy

Add Gaussian noise with a constant variance

Add Gaussian noise with a variance proportional to the input value

Add random noise with a fixed distribution

Remove a random fraction of input features

By freezing the entire network

By fine-tuning the encoder

By fine-tuning the decoder

By reinitializing all layers

Denoising Autoencoder

Variational Autoencoder (VAE)

Convolutional Autoencoder

Sparse Autoencoder

Classify input data

Generate new data samples

Encode data into a lower-dimensional representation

Reconstruct input data

By training only on labeled data

By training only on unlabeled data

By training on a mix of labeled and unlabeled data

By ignoring labels entirely

Autoencoders are faster to compute

Autoencoders can capture nonlinear relationships in data

Autoencoders are simpler to implement

Autoencoders require less memory

F1 score

ROC AUC score

Reconstruction loss

Confusion matrix

Learning rate

Number of classes

Activation function

Image size

Object detection

Image generation

Sentiment analysis

Reinforcement learning

Image classification

Data compression and reconstruction

Speech recognition

Sentiment analysis

Supervised learning

Unsupervised learning

Reinforcement learning

Semi-supervised learning

Image classification

Natural language processing

Data compression

Speech synthesis

Reconstruction loss

Mean Squared Error (MSE) loss

Kullback-Leibler Divergence

Cross-Entropy Loss

Encoding layer

Decoding layer

Compression layer

Bottleneck layer

It generates the reconstructed data.

It adds noise to the input data.

It reduces the dimensionality of the latent space.

It compresses the input data.

Data augmentation

Data reconstruction

Dimensionality reduction

Regularization

Faster training

Improved data compression

Reduced overfitting

Enhanced feature learning

Encoder

Latent space

Decoder

Bottleneck layer

They use a convolutional architecture

They have a bottleneck layer

They learn a probabilistic latent space

They use a larger dataset

Data compression

Data reconstruction

Removing noise from data

Dimensionality reduction

Less than the input dimensionality

Equal to the input dimensionality

Greater than the input dimensionality

Irrelevant to the input dimensionality

Cross-Entropy Loss

Mean Squared Error (MSE) Loss

Kullback-Leibler Divergence

Hinge Loss

Denoising Autoencoder

Variational Autoencoder (VAE)

Sparse Autoencoder

Contractive Autoencoder

Decoder

Bottleneck layer

Encoder

Classifier