<p>XAI methods primarily help in improving models based on better understanding and faster debugging. Thus, as an engineer you can use targeted measures to improve a model.</p>
<p>To the best of my knowledge, there is just one scientific work (see below) that uses explanations of XAI methods directly in the training process of models. Essentially, the paper proposes a novel reasoning approach. First, a model makes a decision. Then, an explanation of the decision is computed. Then, the same (or possibly another) model uses the original input and the explanation to come to a final decision, i.e., in some sense the network ``reflects''/contemplates on its initial decision and its reasons to come to a final conclusion.</p>
<p>&quot;Reflective-Net: Learning from Explanations&quot; by Schneider et al.
<a href="https://arxiv.org/abs/2011.13986" rel="nofollow noreferrer">https://arxiv.org/abs/2011.13986</a></p>
<p>Disclaimer: I am the inventor and first author of the paper.</p>
How to improve model's performance base on XAI methods
<p>I'm learning about explainable AI (XAI) and some of papers I've read say that we can use XAI to improve model's performance. It seems quite a new problem cuz I think when the model has already converged, it's impossible to find a new global minimum and this contradicts the above statement. I want to ask if there is anyways to improve the model's results that relevant to XAI methods? And if there is, how do they work? Tks a lots!!</p>