Bob 2.0 implementation of ISV client model training. Input template ids are strings.
Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
The code for this algorithm in Python
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Given a feature vector, a GMM and a U subspace, computes the Intersession Variability Modeling (ISV) client model. Basically, this algorithm computes the latent variable zi excluding possible session factors (described by the latent variable xi, j).
Specific details can be found in [McCool2013]:
This algorithm relies on the Bob library.
The inputs are:
The output, model, is the latent variable zi (Eq. (31) in McCool2013) that corresponds to the client offset (with the session variations suppressed).
This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.