  Overfitting is a phenomenon that occurs when a machine learning model is
trained for too long and focused too much on the exact fitness of the training
samples to the provided training labels and cannot keep track of the predictive
rules that would be useful on the test data. This phenomenon is commonly
attributed to memorization of particular samples, memorization of the noise,
and forced fitness into a data set of limited samples by using a high number of
neurons. While it is true that the model encodes various peculiarities as the
training process continues, we argue that most of the overfitting occurs in the
process of reconciling sharply defined membership ratios. In this study, we
present an approach that increases the classification accuracy of machine
learning models by allowing the model to negotiate output representations of
the samples with previously determined class labels. By setting up a
negotiation between the models interpretation of the inputs and the provided
labels, we not only increased average classification accuracy but also
decreased the rate of overfitting without applying any other regularization
tricks. By implementing our negotiation paradigm approach to several low regime
machine learning problems by generating overfitting scenarios from publicly
available data sets such as CIFAR 10, CIFAR 100, and MNIST we have demonstrated
that the proposed paradigm has more capacity than its intended purpose. We are
sharing the experimental results and inviting the machine learning community to
explore the limits of the proposed paradigm. We also aim to incentive the
community to exploit the negotiation paradigm to overcome the learning related
challenges in other research fields such as continual learning. The Python code
of the experimental setup is uploaded to GitHub.
