In Ghost BN, the stored statistics
may be suboptimal, since they are updated based on parts
of the batch, instead of the overall batch statistics. A ﬁnal
ﬁne-tuning in “inference mode” allows the network to ﬁne-
tune its weights to the setting that it will be used in during
inference (i.e., to adapt the weights to work well with the
stored statistics).
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats