A survey on Concept-based Approaches For Model Improvement

The focus of recent research has shifted from merely increasing the Deep Neural Networks (DNNs) performance in various tasks to DNNs, which are more interpretable to humans. The field of eXplainable Artificial Intelligence (XAI) has observed various techniques, including saliency-based and concept-based approaches. Concept-based approaches explain the model's decisions in simple human understandable terms called Concepts. Concepts are human interpretable units of data and are the thinking ground of humans. Explanations in terms of concepts enable detecting spurious correlations, inherent biases, or clever-hans. With the advent of concept-based explanations, there have been various concept representation methods and automatic concept discovery algorithms. Some recent methods use concepts for post-hoc model disentanglement evaluation, while others use them for ante-hoc training. The concept-based approaches are new, with many representations coming up, and there is very limited work on Concept-based Model improvement. We provide a systematic review and taxonomy of various concept representations and their discovery algorithms in DNNs, specifically in vision. We also provide details on concept-based model improvement literature, which is the first to survey concept-based model improvement methods.