Flow Pocket trains an exactly invertible RealNVP density model on a curved five-armed pinwheel distribution. Eight affine coupling layers transform data into a standard Gaussian while tracking the exact change-of-variables log determinant.

The benchmark compares held-out negative log-likelihood and generated-sample MMD against a fitted full-covariance Gaussian and a five-component Gaussian mixture. It also measures forward/inverse cycle error to verify that the saved neural transform is numerically invertible.

The flow trained on 40,000 samples and was evaluated on 10,000 independently generated samples.

The eight-coupling-layer RealNVP has 21,536 parameters. Its maximum absolute forward/inverse reconstruction error over 2,000 held-out points was 5.78e-6. MMD uses independently randomized 1,000-sample subsets and a shared median distance bandwidth.