CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning

2026-04-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

The Class-Incremental Concept Bottleneck Model (CI-CBM) addresses catastrophic forgetting in continual learning, particularly in the challenging class incremental learning (CIL) setting, without sacrificing model interpretability or accuracy. CI-CBM employs concept regularization and pseudo-concept generation to maintain interpretable decision processes across incremental learning phases. Evaluated extensively on seven datasets, CI-CBM achieves performance comparable to black-box models and surpasses prior interpretable CIL methods, demonstrating an average 36% accuracy improvement. The model provides interpretable decisions for individual inputs and understandable global decision rules, confirming that human-understandable concepts can be preserved during incremental learning. CI-CBM is effective in both pretrained and non-pretrained scenarios, with its code publicly available.

Key takeaway

For AI Engineers developing continual learning systems, CI-CBM offers a robust solution to the catastrophic forgetting problem in CIL. You should consider integrating CI-CBM's techniques, such as concept regularization and pseudo-concept generation, to build models that are both high-performing and inherently interpretable, especially when deploying systems that require transparent decision-making over time.

Key insights

CI-CBM maintains interpretability and accuracy in class-incremental learning by using concept regularization and pseudo-concept generation.

Principles

• Interpretability need not compromise accuracy.
• Concept regularization aids knowledge retention.
• Pseudo-concepts can mitigate forgetting.

Method

CI-CBM uses concept regularization and pseudo-concept generation to preserve interpretable decision processes and mitigate catastrophic forgetting during class-incremental learning phases.

In practice

• Apply CI-CBM for interpretable continual learning.
• Utilize concept regularization in CIL models.
• Explore pseudo-concept generation for knowledge retention.

Topics

• CI-CBM
• Class Incremental Learning
• Catastrophic Forgetting
• Model Interpretability
• Concept Regularization

Code references

• importAmir/CI-CBM

Best for: AI Engineer, Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer

Related on AIssential

• Evaluating the Impact of Task Granularity on Catastrophic Forgetting in Continual Learning — Learning general categories before specific classes may reduce catastrophic forgetting in continual learning systems.
• DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees — DeltaMem uses residual trees to store LLM agent experiences incrementally, reducing redundancy and improving retrieval.
• Learning, Fast and Slow: Towards LLMs That Adapt Continually — Combining slow parameter updates with fast in-context learning improves LLM adaptation and reduces forgetting.
• Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments — Continual learning benchmarks must isolate online improvement from static model capabilities using expert-validated, latent-structure tasks.
• Teaching AI to Remember: What I Learned Building a Continual Learning Framework for Drug Discovery — Continual learning models can avoid catastrophic forgetting by integrating new knowledge with awareness of existing representations.
• Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models — High-energy, outlier-like samples are more prone to forgetting in continual learning for generative models, and replay is most effective for them.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.