The transition dynamics simply mixes an action and a random sampled latent. It then applies an exponential moving average for temporal persistency, the resulting latent is decoded to image using pretrained generator.

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Existing approaches to addressing shifts, such as concept drift adaptation, are limited by theirreason-agnosticnature.

Inourarticle, weintroduce aRiemannian batch normalization(batchnorm) algorithm, which generalizes the one used in Euclidean nets.

Although various normalization schemes havebeen proposed, theyallfollowacommon theme: normalize acrossspatialdimensions and discard the extracted statistics.

Weconsidertheproblem 9 Figure 5: V ofa SnAK Table 1: the multiplied 103.