We use our method to generate diverse images from scene layouts.
Toggle for our results (CAM-Net) and cIMLE.
We use our method to recover plausible images from a badly compressed image.
Toggle for our results (CAM-Net), DnCNN and cIMLE.
Modelling Joint vs Marginal Distributions
The marginal distribution captures the variability in one variable, whereas the joint distribution captures variability
across multiple variables. The marginal distribution alone does not capture correlations between variables.
Below we show a case where modelling just the marginal distributions leads to spurious samples.
The joint distribution is visualized at the centre, whereas the marginal distributions are visualized around the
boundary. Red points represent samples from the joint distribution and pink points are sampled from independent marginal
distributions. As shown above, pink points may fall outside the probable regions of the joint distribution.
In the case of colourization, the colours of nearby pixels are highly correlated.
Zhang et al. proposed a method that
models marginal distributions only. Below we compare the different samples from Zhang et al. and
CAM-Net which models the joint distribution. As shown, samples from marginal distributions (Zhang et al.) are
spatially inconsistent whereas samples from the joint distribution (CAM-Net) are not.
Implicit Maximum Likelihood Estimation (IMLE)
Below we show a conceptual illustration of how IMLE works.