  Self-supervised contrastive learning frameworks have progressed rapidly over
the last few years. In this paper, we propose a novel mutual information
optimization-based loss function for contrastive learning. We model our
pre-training task as a binary classification problem to induce an implicit
contrastive effect and predict whether a pair is positive or negative. We
further improve the n\"aive loss function using the Majorize-Minimizer
principle and such improvement helps us to track the problem mathematically.
Unlike the existing methods, the proposed loss function optimizes the mutual
information in both positive and negative pairs. We also present a closed-form
expression for the parameter gradient flow and compare the behavior of the
proposed loss function using its Hessian eigen-spectrum to analytically study
the convergence of SSL frameworks. The proposed method outperforms the SOTA
contrastive self-supervised frameworks on benchmark datasets like CIFAR-10,
CIFAR-100, STL-10, and Tiny-ImageNet. After 200 epochs of pre-training with
ResNet-18 as the backbone, the proposed model achieves an accuracy of 86.2\%,
58.18\%, 77.49\%, and 30.87\% on CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet
datasets, respectively, and surpasses the SOTA contrastive baseline by 1.23\%,
3.57\%, 2.00\%, and 0.33\%, respectively.
