Module: tfc.distributions

Classes

class DeepFactorized: Fully factorized distribution based on neural network cumulative.

class MonotonicAdapter: Adapt a continuous distribution via an ascending monotonic function.

class NoisyDeepFactorized: DeepFactorized that is convolved with uniform noise.

class NoisyLaplace: Laplacian distribution with additive i.i.d. uniform noise.

class NoisyLogistic: Logistic distribution with additive i.i.d. uniform noise.

class NoisyLogisticMixture: Mixture of logistic distributions with additive i.i.d. uniform noise.

class NoisyMixtureSameFamily: Mixture of distributions with additive i.i.d. uniform noise.

class NoisyNormal: Gaussian distribution with additive i.i.d. uniform noise.

class NoisyNormalMixture: Mixture of normal distributions with additive i.i.d. uniform noise.

class NoisyRoundedDeepFactorized: Rounded DeepFactorized + uniform noise.

class NoisyRoundedNormal: Rounded normal distribution + uniform noise.

class NoisySoftRoundedDeepFactorized: Soft rounded DeepFactorized + uniform noise.

class NoisySoftRoundedNormal: Soft rounded normal distribution + uniform noise.

class RoundAdapter: Continuous density function + round.

class SoftRoundAdapter: Differentiable approximation to round.

class UniformNoiseAdapter: Additive i.i.d. uniform noise adapter distribution.

estimate_tails(...): Estimates approximate tail quantiles.

lower_tail(...): Approximates lower tail quantile for range coding.

quantization_offset(...): Computes distribution-dependent quantization offset.

upper_tail(...): Approximates upper tail quantile for range coding.