Diffusion Architecture QUIZ (MCQ QUESTIONS AND ANSWERS)

VAEs do not use a generative model

Diffusion models do not use a latent space

VAEs use a Gaussian distribution for noise

Diffusion models do not use a likelihood term

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Transformer models

Autoencoders

To minimize the reconstruction loss

To maximize the likelihood of the observed data

To remove noise from the input text

To generate text that is easy to understand

Faster training

Better language understanding

Ability to handle short texts only

High-quality text generation

Gaussian diffusion

Markov diffusion

Laplace diffusion

Brownian diffusion

To add noise to the data

To generate data samples

To calculate the likelihood of the observed data

To remove noise from the data

They require less computational power

They are more interpretable

They can generate high-quality samples

They are less prone to overfitting

It remains constant

It increases

It decreases

It oscillates randomly

Faster computation

Improved fine details in inpainted regions

Smaller model size

Reduced training data requirements

Diffusion modeling does not use noise

Diffusion modeling uses a generative approach

Traditional methods use a diffusion process

Traditional methods use a fixed noise model

To make the denoising process slower

To add noise to the clean images

To remove noise from the noisy images

To improve the quality of noisy images

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Transformer models

Autoencoders

To generate data samples

To calculate the likelihood of the observed data

To add noise to the data

To remove noise from the data

To minimize the reconstruction error

To maximize the likelihood of the observed data

To remove noise from the input data

To learn a compressed representation of the data

They are computationally efficient

They require a large amount of training data

They are prone to overfitting

They are not suitable for image generation

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

It controls the learning rate of the model

It determines the number of gradient updates

It sets the duration of each noise level

It controls the batch size

Faster computation

Better quality inpainting results

Simplicity of implementation

Lower memory requirements

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Transformer models

Autoencoders

By using a fixed noise schedule

By increasing the noise level with each step

By decreasing the noise level with each step

By randomly selecting noise levels

Faster training

Higher likelihood of data

Simplicity of architecture

Parallel generation of data

To add noise to the data

To remove noise from the data

To generate data samples

To calculate the likelihood of the observed data

To add noise to the data

To remove noise from the data

To gradually generate data samples

To increase the likelihood of the observed data

Image denoising

Image inpainting

Anomaly detection

Speech recognition

High computational cost

Lack of interpretability

Difficulty in handling sequential data

Overfitting to the training data

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Transformer models

Autoencoders

Increasing the noise level during training

Decreasing the noise level during training

Adding noise to the input data

Removing noise from the input data

Gaussian diffusion

Markov diffusion

Laplace diffusion

Brownian diffusion

They are computationally efficient

They have a simple architecture

They can generate high-quality samples

They require minimal training data

Minimize the reconstruction loss

Maximize the likelihood of the observed data

Minimize the noise level

Maximize the number of diffusion steps