Given the prevelance of big data and increased computability, the application of Deep Neural Network (DNN) methods are a commonality. However, the intricacies and depth of DNN architectures lead to results that lack inherent interpretability. In pivotal domains such as healthcare and finance, interpretability is crucial and thus the application of eXplainable Artificial Intelligence (XAI) to extract valuable insights from the DNN models is widespread [1, 2]. The Path-Integrated Gradients (Path-IG) [3] formulation presents axiomatic properties that are upheld solely by pathbased explanation methods. The Out-of-Distribution (OoD) problem is prevalent in the application of path-based explanation methods [4]; here the intuition is that traveling along a straight line path can incur irregular gradients and thus provide noisy attribution values [5]. Another known limitation of many Integrated Gradient (IG) [3] based approaches is the selection of a baseline reference; thus the Adversarial Gradient Integration (AGI) [6] method relaxes this constraint by generating baselines in adversarial classes. We note that AGI utilizes the path-based approach for generating counterfactual examples, and for this reason will be a primary baseline for our proposed method throughout this paper.