Using disentangled learning to train an interpretable deep learning model

A computer program product for training an interpretable deep learning model for a machine learning system, the computer program product comprising:

• one or more non-transitory computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
• receiving an input set of data;
• providing the input set of data to a deep neural network model;
• extracting features from the deep neural network model;
• generating a latent space of vectors comprising the extracted features;
• feeding the latent space of vectors generated from extracted features of the deep neural network model to a task-specific model, wherein the task-specific model is a low-complexity and linear learning model;
• generating interpretable predictions of feature dimensions from the task-specific model;
• reconstructing the input set of data using a decoder module;
• determining a reconstruction error loss from reconstructing the input set of data;
• determining a classification loss or a regression loss from a task specific output set of data;
• and
• training an autoencoder, the decoder module, and the low-complexity learning model, using a combination of (i) the reconstruction error loss and (ii) the classification loss or the regression loss.

A computer server, comprising:

• a network connection;
• one or more computer readable storage media;
• a processor coupled to the network connection and coupled to the one or more computer readable storage media;
• and
• a computer program product comprising program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
• receiving an input set of data;
• providing the input set of data to a deep neural network model;
• extracting features from the deep neural network model;
• generating a latent space of vectors comprising the extracted features;
• feeding the latent space of vectors generated from extracted features of the deep neural network model to a task-specific model to a task-specific model, wherein the task-specific model is a low-complexity and linear learning model;
• generating interpretable predictions of feature dimensions from the task-specific model;
• reconstructing the input set of data using a decoder module;
• determining a reconstruction error loss from reconstructing the input set of data;
• determining a classification loss or a regression loss from a task specific output set of data;
• and
• training an autoencoder, the decoder module, and the low-complexity learning model, using a combination of (i) the reconstruction error loss and (ii) the classification loss or the regression loss.

A method of training an interpretable deep learning model for a machine learning system, comprising:

• receiving an input set of data;
• providing the input set of data to a Beta variational autoencoder;
• generating by the Beta variational autoencoder, an output representation of the input set of data;
• processing the output representation generated by the Beta variational autoencoder, using a low-complexity linear learning model;
• determining by the low-complexity learning model a task specific output set of data;
• providing an interpretation of the input set of data based on the task specific output set of data;
• reconstructing the input set of data using a decoder module;
• determining a reconstruction error loss from reconstructing the input set of data;
• determining a classification loss or a regression loss from the task specific output set of data;
• and
• training an autoencoder, the decoder module, and the low-complexity learning model, using a combination of (i) the reconstruction error loss and (ii) the classification loss or the regression loss.

A computer program product for training an interpretable deep learning model for an artificial intelligence computing system, the computer program product comprising:

• one or more non-transitory computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
• receiving an input set of data;
• providing the input set of data to a Beta variational autoencoder;
• generating by the Beta variational autoencoder, an output representation of the input set of data;
• processing the output representation generated by the Beta variational autoencoder using a low-complexity learning model that is linear;
• determining by the low-complexity learning model a task specific output set of data;
• providing an interpretation of the input set of data based on the task specific output set of data;
• reconstructing the input set of data using a decoder module;
• determining a reconstruction error loss from reconstructing the input set of data;
• determining a classification loss or a regression loss from the task specific output set of data;
• and
• training the Beta variational autoencoder, the decoder module, and the low-complexity learning model using a combination of (i) the reconstruction error loss and (ii) the classification loss or the regression loss.