Device and method for classifying an input signal using an invertible factorization model

A classifier, configured to:

• determine a latent representation based on the input signal using an invertible factorization model comprised in the classifier, wherein the latent representation includes a plurality of factors, and wherein the invertible factorization model includes:
• a plurality of functions, wherein any one function from the plurality of functions is continuous and almost everywhere continuously differentiable,
• wherein the function is further configured to accept either the input signal or at least one factor of the plurality of factors provided by another function of the plurality of functions as an input and
• wherein the function is further configured to provide at least one factor of the plurality of factors, wherein the at least one factor is either provided as at least part of the latent representation or is provided as at least part of an input of another function of the plurality of functions,
• wherein there exists an inverse function that corresponds with the function, is continuous, is almost everywhere continuously differentiable and is configured to determine the input of a layer based on the at least one factor provided from the function;
• determine the output signal based on the latent representation using an internal classifier comprised in the classifier.

A training system configured to train a classifier, the classifier configured to:

• determine a latent representation based on the input signal using an invertible factorization model comprised in the classifier, wherein the latent representation includes a plurality of factors, and wherein the invertible factorization model includes:
• a plurality of functions, wherein any one function from the plurality of functions is continuous and almost everywhere continuously differentiable,
• wherein the function is further configured to accept either the input signal or at least one factor of the plurality of factors provided by another function of the plurality of functions as an input and
• wherein the function is further configured to provide at least one factor of the plurality of factors, wherein the at least one factor is either provided as at least part of the latent representation or is provided as at least part of an input of another function of the plurality of functions,
• wherein there exists an inverse function that corresponds with the function, is continuous, is almost everywhere continuously differentiable and is configured to determine the input of a layer based on the at least one factor provided from the function;
• determine the output signal based on the latent representation using an internal classifier comprised in the classifier;
• the training system configured to train the classifier based on a first difference between a determined output signal and a desired output, wherein the determined output signal is determined for a training input signal and the desired output signal characterizes a desired classification of the training input signal.

A non-transitory machine-readable storage medium on which is stored a computer program for determining an output signal for an input signal using a classifier, wherein the output signal characterizes a classification of the input signal, the computer program, when executed by a processor, causing the processor to perform the following steps:

• determining a latent representation based on the input signal using an invertible factorization model comprised in the classifier, wherein the latent representation includes a plurality of factors, and wherein the invertible factorization model includes:
• a plurality of functions, wherein any one function from the plurality of functions is continuous and almost everywhere continuously differentiable,
• wherein the function is further configured to accept either the input signal or at least one factor of the plurality of factors provided by another function of the plurality of functions as an input and
• wherein the function is further configured to provide at least one factor of the plurality of factors, wherein the at least one factor is either provided as at least part of the latent representation or is provided as at least part of an input of another function of the plurality of functions,
• wherein there exists an inverse function that corresponds with the function, is continuous, is almost everywhere continuously differentiable and is configured to determine the input of a layer based on the at least one factor provided from the function;
• determining the output signal based on the latent representation using an internal classifier comprised in the classifier.