Aditya Vardhan Several approaches can be employed to enhance the interpretability of deep neural networks and improve understanding of their decision-making process. These include feature visualization techniques to visualize the learned representations of the network, layer-wise relevance propagation methods to identify the importance of input features for making predictions, and saliency mapping techniques such as gradient-based methods to highlight important regions in input data. Additionally, employing simpler or more transparent models as proxies for complex neural networks and integrating domain knowledge into the model architecture or interpretation process can enhance interpretability. By combining these approaches, researchers can gain deeper insights into the inner workings of deep neural networks and make more informed decisions based on their outputs.

To enhance the interpretability of deep neural networks, approaches such as feature visualization, layer-wise relevance propagation, and attention mechanisms can be employed. These methods help in visualizing and understanding the features and patterns that the network focuses on for making decisions. Additionally, using simpler models or model distillation techniques can also aid in making the decision-making process more transparent and understandable.

Enhancing the interpretability of deep neural networks is crucial for better understanding their decision-making process, especially in applications where transparency and trustworthiness are paramount. Here are some approaches to achieve this:

By incorporating these approaches, it is possible to enhance the interpretability of deep neural networks, making them more transparent and understandable for stakeholders and end-users.

Please follow me if it's helpful. All the very best. Regards, Safiul