Cs7643 Quiz 7 Exam NEwEst 2025 ComplEtE 31 QuEstioNs aNd
CorrECt dEtailEd aNswErs (VErifiEd aNswErs) |alrEady
GradEd a+||BraNd NEw VErsioN!!
Explicit vs Implicit Density - CORRECT ANSWER-Explicit: Explicitly define and solve for
Pmodel(x|θ), given an explicit likelihood that can be maximized
Implicit: Learn a parameterized generation model that can sample from the joint
distribution Pmodel(x|θ) without explicitly defining Pmodel(x|θ)
Tractable Density Estimation - CORRECT ANSWER-- Explicit Density
- Simplify joint distribution into factorized model of simpler components and optimize
with respect to simpler components
- PixelRNN/PixelCNN
Approximate Density Estimation - CORRECT ANSWER-- Explicit Density
- Learn distributions that approximate true joint distribution
- Variational Autoencoder
Implicit Density Estimation - CORRECT ANSWER-- We don't model density itself, but
can sample from the distribution
- GAN
, Factorized Models for Images Downsides - CORRECT ANSWER-1. Slow sequential
generation process
2. Only considers few context pixels
PixelRNN - CORRECT ANSWER-Explicit & Tractable
Uses chain rule to decompose the likelihood of an image x into a product of 1-d
distributions
Similar to language models
Requires ordering of variables!
Training: pixel depends implicitly on the pixels above and to the left.
Generating: Generates pixels one at a time, very slow, only considers the pixels
immediately adjacent when generating
Pixel CNN - CORRECT ANSWER-Idea: Represent conditional distribution as a
convolution layer
- Considers larger context (receptive field)
- Practically can be implemented by applying a mask, zeroing out "future" pixels
Disadvantages:
- Faster training, but still slow generation
- Limited to smaller images
Discriminative vs. Generative Models - CORRECT ANSWER-1. Discriminative models
model P(y|x)
CorrECt dEtailEd aNswErs (VErifiEd aNswErs) |alrEady
GradEd a+||BraNd NEw VErsioN!!
Explicit vs Implicit Density - CORRECT ANSWER-Explicit: Explicitly define and solve for
Pmodel(x|θ), given an explicit likelihood that can be maximized
Implicit: Learn a parameterized generation model that can sample from the joint
distribution Pmodel(x|θ) without explicitly defining Pmodel(x|θ)
Tractable Density Estimation - CORRECT ANSWER-- Explicit Density
- Simplify joint distribution into factorized model of simpler components and optimize
with respect to simpler components
- PixelRNN/PixelCNN
Approximate Density Estimation - CORRECT ANSWER-- Explicit Density
- Learn distributions that approximate true joint distribution
- Variational Autoencoder
Implicit Density Estimation - CORRECT ANSWER-- We don't model density itself, but
can sample from the distribution
- GAN
, Factorized Models for Images Downsides - CORRECT ANSWER-1. Slow sequential
generation process
2. Only considers few context pixels
PixelRNN - CORRECT ANSWER-Explicit & Tractable
Uses chain rule to decompose the likelihood of an image x into a product of 1-d
distributions
Similar to language models
Requires ordering of variables!
Training: pixel depends implicitly on the pixels above and to the left.
Generating: Generates pixels one at a time, very slow, only considers the pixels
immediately adjacent when generating
Pixel CNN - CORRECT ANSWER-Idea: Represent conditional distribution as a
convolution layer
- Considers larger context (receptive field)
- Practically can be implemented by applying a mask, zeroing out "future" pixels
Disadvantages:
- Faster training, but still slow generation
- Limited to smaller images
Discriminative vs. Generative Models - CORRECT ANSWER-1. Discriminative models
model P(y|x)
