METHODS, SYSTEMS, APPARATUS AND ARTICLES OF MANUFACTURE TO APPLY A REGULARIZATION LOSS IN MACHINE LEARNING MODELS
Methods, systems, apparatus and articles of manufacture are disclosed herein to apply a regularization loss in machine learning models. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to 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 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 train the neural network for use in continual learning with the at least one neural network filter corrected using the survival loss function.
1 . An apparatus, comprising:
at least one memory;
instructions in the apparatus; and
processor circuitry to execute the instructions to:
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 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
train the neural network for use in continual learning with the at least one neural network filter corrected using the survival loss function.
2 . The apparatus of claim 1 , wherein the processor circuitry is to optimize the neural network using an adaptive learning rate optimizer.
3 . The apparatus of claim 2 , wherein the adaptive learning rate optimizer is an Adam optimizer, the Adam optimizer used in conjunction with an L2 regularizer.
4 . The apparatus of claim 1 , wherein the survival loss function includes at least one term for a total number of filters in the neural network, a filter norm, or the one or more hyperparameters defining the minimum filter norm.
5 . The apparatus of claim 1 , wherein the neural network is a residual neural network.
6 . The apparatus of claim 1 , wherein the processor circuitry is to determine a total loss function, the total loss function a regularizer-based loss function including the survival loss function to decrease filter death.
7 . The apparatus of claim 6 , wherein the total loss function includes at least one of a cross entropy loss, weight decay, or a survival loss impact hyperparameter.
8 .- 14 . (canceled)
15 . 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.
16 . The non-transitory computer readable storage medium as defined in claim 15 , wherein the computer readable instructions, when executed, cause the one or more processors to optimize the neural network using an adaptive learning rate optimizer.
17 . The non-transitory computer readable storage medium as defined in claim 16 , wherein the adaptive learning rate optimizer is an Adam optimizer, the Adam optimizer used in conjunction with an L2 regularizer.
18 . The non-transitory computer readable storage medium as defined in claim 15 , wherein the neural network is a residual neural network.
19 . The non-transitory computer readable storage medium as defined in claim 16 , wherein the computer readable instructions, when executed, cause the one or more processors to determine a total loss function, the total loss function a regularizer-based loss function including the survival loss function to decrease filter death.
20 . The non-transitory computer readable storage medium as defined in claim 19 , wherein the total loss function includes at least one of a cross entropy loss, weight decay, or a survival loss impact hyperparameter.
21 . 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.
22 . The apparatus of claim 21 , further including means for optimizing the neural network using an adaptive learning rate optimizer.
23 . The apparatus of claim 22 , wherein the adaptive learning rate optimizer is an Adam optimizer, the Adam optimizer used in conjunction with an L2 regularizer.
24 . The apparatus of claim 21 , wherein the survival loss function includes at least one term for a total number of filters in the neural network, a filter norm, or the one or more hyperparameters defining the minimum filter norm.
25 . The apparatus of claim 21 , wherein the neural network is a residual neural network.
26 . The apparatus of claim 21 , wherein the means for correcting the filter norm values further includes determining a total loss function, the total loss function a regularizer-based loss function including the survival loss function to decrease filter death.
27 . The apparatus of claim 26 , wherein the total loss function includes at least one of a cross entropy loss, weight decay, or a survival loss impact hyperparameter.