18893616. OUT-OF-DISTRIBUTION DETECTION WITH GENERATIVE MODELS (The Toronto-Dominion Bank)
OUT-OF-DISTRIBUTION DETECTION WITH GENERATIVE MODELS
Organization Name
Inventor(s)
Jesse Cole Cresswell of TORONTO CA
Brendan Leigh Ross of TORONTO CA
Gabriel Loaiza Ganem of TORONTO CA
Anthony Lawrence Caterini of TORONTO CA
Hamidreza Kamkari of TORONTO CA
OUT-OF-DISTRIBUTION DETECTION WITH GENERATIVE MODELS
This abstract first appeared for US patent application 18893616 titled 'OUT-OF-DISTRIBUTION DETECTION WITH GENERATIVE MODELS
Original Abstract Submitted
Generative models are used to determine whether a data sample is in-distribution or out-of-distribution with respect to a training data set. To address potential errors in generative models that attribute high likelihoods to known out-of-distribution data samples, in addition to the likelihood for a data sample, the local intrinsic dimensionality is also evaluated for the data sample. A data sample is determined to belong to the distribution of the training data when the data sample both has sufficient likelihood and local intrinsic dimensionality around its region in the generative model. Different actions may then be determined for the data sample with respect to a data application model based on whether the data sample is in- or out-of-distribution.
