Abstract
Neural networks suffer from catastrophic forgetting problem when
deployed in a continual learning scenario where new batches of data
arrive over time; however they are of different distributions from the
previous data used for training the neural network. For assessing the
performance of a model in a continual learning scenario, two aspects are
important (i) to compute the difference in data distribution between a
new and old batch of data and (ii) to understand the retention and
learning behavior of deployed neural networks. Current techniques
indicate the novelty of a new data batch by comparing its statistical
properties with that of the old batch in the input space. However, it is
still an open area of research to consider the perspective of a deployed
neural network’s ability to generalize on the unseen data samples. In
this work, we report a dataset distance measuring technique that
indicates the novelty of a new batch of data while considering the
deployed neural network’s perspective. We propose the construction of
perspective histograms which are a vector representation of the data
batches based on the correctness and confidence in the prediction of the
deployed model. We have successfully tested the hypothesis empirically
on image data coming MNIST Digits, MNIST Fashion, CIFAR10, for its
ability to detect data perturbations of type rotation, Gaussian blur,
and translation. Upon new data, given a model and its training data, we
have proposed and evaluated four new scoring schemes, retention score
(R), learning score (L), Oscore and SP-score for studying how much the
model can retain its performance on past data, how much it can learn new
data, the combined expression for the magnitude of retention and
learning and stability-plasticity characteristics respectively. The
scoring schemes have been evaluated MNIST Digits and MNIST Fashion data
sets on different types of neural network architectures based on the
number of parameters, activation functions, and learning loss functions,
and an instance of a typical analysis report is presented. Machine
learning model maintenance is a reality in production systems in the
industry, and we hope our proposed methodology offers a solution to the
need of the day in this aspect.