Methods, systems, apparatus and articles of manufacture to apply a regularization loss in machine learning models

A non-transitory computer readable storage medium comprising computer readable instructions which, when executed, cause a processor to at least:

• identify at least one neural network filter with filter norm values below a filter norm threshold, the filter norm values corresponding to filter functionality, a higher level of filter functionality corresponding to decreased filter death;
• correct the filter norm values by applying a survival loss function, the survival loss function including one or more hyperparameters;
• reduce the filter death by adjusting the one or more hyperparameters used to define a minimum filter norm for identification of filter functionality based on neural network filter performance, a functional filter to return non-zero parameter values indicating reduction of filter death;
• and
• train the neural network for use in continual learning with the at least one or more neural network filter corrected using the survival loss function.

An apparatus, comprising:

• means for identifying at least one neural network filter with filter norm values below a filter norm threshold, the filter norm values corresponding to filter functionality, a higher level of filter functionality corresponding to decreased filter death;
• means for correcting the filter norm values by applying a survival loss function,
• the survival loss function including one or more hyperparameters;
• means for reducing filter death by adjusting the one or more hyperparameters used to define a minimum filter norm for identification of filter functionality, the adjustment based on neural network filter performance, a functional filter to return non-zero parameter values indicating reduction of filter death;
• and
• means for training the neural network for use in continual learning with the at least one neural network filter corrected using the survival loss function.