  Is it possible to train several classifiers to perform meaningful
crowd-sourcing to produce a better prediction label set without ground-truth
annotation? This paper will modify the contrastive learning objectives to
automatically train a self-complementing ensemble to produce a state-of-the-art
prediction on the CIFAR10 and CIFAR100-20 tasks. This paper will present a
straightforward method to modify a single unsupervised classification pipeline
to automatically generate an ensemble of neural networks with varied decision
boundaries to learn a more extensive feature set of classes. Loss Function
Entropy Regularization (LFER) are regularization terms to be added to the
pre-training and contrastive learning loss functions. LFER is a gear to modify
the entropy state of the output space of unsupervised learning, thereby
diversifying the latent representation of decision boundaries of neural
networks. Ensemble trained with LFER has higher successful prediction accuracy
for samples near decision boundaries. LFER is an adequate gear to perturb
decision boundaries and has produced classifiers that beat state-of-the-art at
the contrastive learning stage. Experiments show that LFER can produce an
ensemble with accuracy comparable to the state-of-the-art yet have varied
latent decision boundaries. It allows us to perform meaningful verification for
samples near decision boundaries, encouraging the correct classification of
near-boundary samples. By compounding the probability of correct prediction of
a single sample amongst an ensemble of neural network trained, our method can
improve upon a single classifier by denoising and affirming correct feature
mappings.
