Label-Free Concept Bottleneck Models (2304.06129v2)

Abstract: Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to human-understandable concepts. However, existing CBMs and their variants have two crucial limitations: first, they need to collect labeled data for each of the predefined concepts, which is time consuming and labor intensive; second, the accuracy of a CBM is often significantly lower than that of a standard neural network, especially on more complex datasets. This poor performance creates a barrier for adopting CBMs in practical real world applications. Motivated by these challenges, we propose Label-free CBM which is a novel framework to transform any neural network into an interpretable CBM without labeled concept data, while retaining a high accuracy. Our Label-free CBM has many advantages, it is: scalable - we present the first CBM scaled to ImageNet, efficient - creating a CBM takes only a few hours even for very large datasets, and automated - training it for a new dataset requires minimal human effort. Our code is available at https://github.com/Trustworthy-ML-Lab/Label-free-CBM. Finally, in Appendix B we conduct a large scale user evaluation of the interpretability of our method.

An Expert Analysis of "Label-Free Concept Bottleneck Models"

The paper "Label-Free Concept Bottleneck Models," presented by Tuomas Oikarinen, Lam M. Nguyen, Subhro Das, and Tsui-Wei Weng, introduces a sophisticated approach to Concept Bottleneck Models (CBMs), aiming to bridge the interpretability gap in deep neural networks (DNNs) without the need for labeled concept data. The foundation of this research is the innovative development of the Label-free CBM, which addresses major limitations of traditional CBMs, specifically the dependency on concept-labeled data and subpar accuracy in complex datasets.

Concept Bottleneck Models and Their Constraints

CBMs enhance the interpretability of DNNs by correlating neurons in a hidden layer with human-understandable concepts. Nevertheless, traditional implementations of CBMs face two primary challenges: the requirement for labeled data for each concept, which is both time-intensive and expensive, and a marked decrease in model accuracy on intricate datasets compared to standard neural networks. These issues hinder the practical application of CBMs across various domains.

Introduction of the Label-Free CBM Framework

The authors propose a Label-free CBM framework circumventing the need for labeled concept data. This innovative framework transforms any neural network into a CBM while maintaining accuracy akin to conventional DNNs. Key attributes of Label-free CBM include scalability to large datasets like ImageNet, operational efficiency—with CBM creation achievable in mere hours—and automation necessitating minimal human intervention for new datasets.

Significance and Implementation:

1. Scalability: The framework's capability to scale CBMs to ImageNet, a prominent benchmark in computer vision, underscores its robustness and operational feasibility in large-scale data environments.
2. Efficiency and Automation: By eliminating the necessity for exhaustive manual concept labeling, the proposed framework significantly reduces the human labor involved in training CBMs. The utilization of foundation models enhances this capability, promoting broader applicability across diverse datasets.

The research introduces a structured methodology of four steps to transform a neural network into a Label-free CBM: concept set creation and filtering, computation of embeddings and a concept matrix, learning projection weights for the Concept Bottleneck Layer (CBL), and training a sparse final layer. These steps systematically address the interpretability challenges while preserving the accuracy of the neural networks.

Empirical Analysis and Findings

Testing across five datasets—including CIFAR-10, CIFAR-100, CUB, Places365, and ImageNet—demonstrates the model's effectiveness. Notably, results on ImageNet show a 72% top-1 accuracy, comparable to standard neural networks, yet with augmented interpretability. The research distinctly illustrates how the Label-free CBM provides more meaningful and concise explanations than conventional networks.

Future Implications and Methodological Innovations

The implications of this research extend both practical and theoretical dimensions. Practically, it offers a scalable solution for interpretability in large-scale neural networks without reliance on manually annotated data. Theoretically, it pioneers the integration of foundation models for reducing the semantic gap in neural network outputs.

Potential Advancements:

• Model Debugging and Editing: The manual editing of final layer weights based on user evaluations exemplifies a new direction in model finetuning, suggesting that practitioner-driven modifications can improve model accuracy on specific subsets of data without extensive retraining.
• Exploration of Foundation Models: Future exploration could refine the integration of GPT-3 generated concepts, potentially improving domain-specific model interpretation and enhancing the practicality of CBMs in specialized fields.

In summary, "Label-Free Concept Bottleneck Models" presents a compelling advancement in the domain of interpretable machine learning. It offers a valuable framework for enhancing the transparency of model predictions, thereby fostering the deployment of neural networks in settings where explicability is paramount. This paper stands as a significant contribution to the ongoing discourse on making artificial intelligence models not only more powerful but also more understandable and accountable.