One way to improve model interpretability in PyTorch is by utilizing techniques like gradient-based methods such as Integrated Gradients or Grad-CAM, which provide insights into the importance of input features. These methods help to identify which parts of the input contributed most to the model's decision. Additionally, PyTorch provides support for feature visualization techniques such as activation maximization or occlusion sensitivity, which allow us to visualize what patterns or features the model is focusing on during inference.

Another approach to enhance model interpretability is by utilizing techniques like attention mechanisms in PyTorch. Attention mechanisms allow the model to learn to focus on relevant parts of the input, providing additional insight into the decision-making process. This can be particularly useful in tasks like natural language processing, where understanding which parts of the input influenced the model's output is important.

PyTorch also offers packages like Captum, which provide model interpretability tools. Captum supports various attribution methods like gradient attribution and SHAP values, which help in understanding the contribution of each input feature to the model's prediction. These tools not only aid in interpreting the model's output but also assist in debugging and improving model performance.