Incremental Residual Concept Bottleneck Models

arXiv:2404.08978v1 Announce Type: new
Abstract: Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. Multimodal pre-trained models can match visual representations with textual concept embeddings, allowing for obtaining the interpretable concept bottleneck without the expertise concept annotations. Recent research has focused on the concept bank establishment and the high-quality concept selection. However, it is challenging …

abstract arxiv box concept concepts cs.ai cs.lg decision embeddings incremental making map match multimodal networks neural networks predictions pre-trained models process residual set textual transparency type visual