LogPoissonLikelihoodDistance#
- class deepinv.optim.LogPoissonLikelihoodDistance(N0=1024.0, mu=1 / 50.0)[source]#
Bases:
Distance
Log-Poisson negative log-likelihood.
\[\distance{z}{y} = N_0 (1^{\top} \exp(-\mu z)+ \mu \exp(-\mu y)^{\top}x)\]Corresponds to LogPoissonNoise with the same arguments N0 and mu. There is no closed-form of the prox known.