Editable Concept Bottleneck Models

• URL: http://arxiv.org/abs/2405.15476v2
• Date: Fri, 04 Oct 2024 13:52:33 GMT
• Title: Editable Concept Bottleneck Models
• Authors: Lijie Hu, Chenyang Ren, Zhengyu Hu, Hongbin Lin, Cheng-Long Wang, Hui Xiong, Jingfeng Zhang, Di Wang,
• Abstract summary: Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. In many scenarios, we always need to remove/insert some training data or new concepts from trained CBMs due to different reasons, such as privacy concerns, data mislabelling, spurious concepts, and concept annotation errors. We propose Editable Concept Bottleneck Models (ECBMs) to address these challenges. Specifically, ECBMs support three different levels of data removal: concept-label-level, concept-level, and data-level.
• Score: 36.38845338945026
• License:
• Abstract: Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. However, most previous studies focused on cases where the data, including concepts, are clean. In many scenarios, we always need to remove/insert some training data or new concepts from trained CBMs due to different reasons, such as privacy concerns, data mislabelling, spurious concepts, and concept annotation errors. Thus, the challenge of deriving efficient editable CBMs without retraining from scratch persists, particularly in large-scale applications. To address these challenges, we propose Editable Concept Bottleneck Models (ECBMs). Specifically, ECBMs support three different levels of data removal: concept-label-level, concept-level, and data-level. ECBMs enjoy mathematically rigorous closed-form approximations derived from influence functions that obviate the need for re-training. Experimental results demonstrate the efficiency and effectiveness of our ECBMs, affirming their adaptability within the realm of CBMs.

Related papers

• How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization? [91.49559116493414]
  We propose a novel Concept-Incremental text-to-image Diffusion Model (CIDM) It can resolve catastrophic forgetting and concept neglect to learn new customization tasks in a concept-incremental manner. Experiments validate that our CIDM surpasses existing custom diffusion models.
  arXiv  Detail & Related papers  (2024-10-23T06:47:29Z)
• Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models [57.86303579812877]
  Concept Bottleneck Models (CBMs) ground image classification on human-understandable concepts to allow for interpretable model decisions. Existing approaches often require numerous human interventions per image to achieve strong performances. We introduce a trainable concept realignment intervention module, which leverages concept relations to realign concept assignments post-intervention.
  arXiv  Detail & Related papers  (2024-05-02T17:59:01Z)
• Incremental Residual Concept Bottleneck Models [29.388549499546556]
  Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts. We propose the Incremental Residual Concept Bottleneck Model (Res-CBM) to address the challenge of concept completeness. Our approach can be applied to any user-defined concept bank, as a post-hoc processing method to enhance the performance of any CBMs.
  arXiv  Detail & Related papers  (2024-04-13T12:02:19Z)
• Separable Multi-Concept Erasure from Diffusion Models [52.51972530398691]
  We propose a Separable Multi-concept Eraser (SepME) to eliminate unsafe concepts from large-scale diffusion models. The latter separates optimizable model weights, making each weight increment correspond to a specific concept erasure. Extensive experiments indicate the efficacy of our approach in eliminating concepts, preserving model performance, and offering flexibility in the erasure or recovery of various concepts.
  arXiv  Detail & Related papers  (2024-02-03T11:10:57Z)
• Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning [17.982131928413096]
  Concept Bottleneck Models (CBMs) aim to deliver interpretable and interventionable predictions by bridging features and labels with human-understandable concepts. CBMs suffer from information leakage, where unintended information beyond the concepts are leaked to the subsequent label prediction. This paper proposes a new paradigm of CBMs, namely SupCBM, which achieves label predication via predicted concepts and a deliberately-designed intervention matrix.
  arXiv  Detail & Related papers  (2024-02-03T03:50:58Z)
• ConcEPT: Concept-Enhanced Pre-Training for Language Models [57.778895980999124]
  ConcEPT aims to infuse conceptual knowledge into pre-trained language models. It exploits external entity concept prediction to predict the concepts of entities mentioned in the pre-training contexts. Results of experiments show that ConcEPT gains improved conceptual knowledge with concept-enhanced pre-training.
  arXiv  Detail & Related papers  (2024-01-11T05:05:01Z)
• Auxiliary Losses for Learning Generalizable Concept-based Models [5.4066453042367435]
  Concept Bottleneck Models (CBMs) have gained popularity since their introduction. CBMs essentially limit the latent space of a model to human-understandable high-level concepts. We propose cooperative-Concept Bottleneck Model (coop-CBM) to overcome the performance trade-off.
  arXiv  Detail & Related papers  (2023-11-18T15:50:07Z)
• Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
  Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation. We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
  arXiv  Detail & Related papers  (2023-11-08T20:41:18Z)
• I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks [2.9398911304923447]
  Concept bottleneck models (CBMs) provide explainability and intervention during inference by correcting predicted, intermediate concepts. This makes CBMs attractive for high-stakes decision-making. We take the quality assessment of fetal ultrasound scans as a real-life use case for CBM decision support in healthcare.
  arXiv  Detail & Related papers  (2022-11-19T09:31:19Z)
• Post-hoc Concept Bottleneck Models [11.358495577593441]
  Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts and use the concepts to make predictions. CBMs are restrictive in practice as they require concept labels in the training data to learn the bottleneck and do not leverage strong pretrained models. We show that we can turn any neural network into a PCBM without sacrificing model performance while still retaining interpretability benefits.
  arXiv  Detail & Related papers  (2022-05-31T00:29:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.