Selective Concept Bottleneck Models Without Predefined Concepts
Authors: Simon Schrodi, Julian Schur, Max Argus, Thomas Brox
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated UCBM on diverse image classification tasks and compared it to relevant baselines. We show that UCBMs outperform prior work and narrow the gap to their black-box counterparts, while relying on substantially fewer concepts globally in their classification (Section 3.1). Then, we demonstrate the interpretability qualitatively as well as through a user study (Section 3.2). |
| Researcher Affiliation | Academia | Simon Schrodi EMAIL University of Freiburg Julian Schur EMAIL University of Freiburg, Karlsruhe Institute of Technology Max Argus EMAIL University of Freiburg Thomas Brox EMAIL University of Freiburg |
| Pseudocode | No | The paper describes mathematical formulations for its methods (e.g., Equation 1 for unsupervised concept discovery, Equation 6 for the final interpretable classifier, and Equations 3, 4, 5 for concept selection mechanisms), but does not contain explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor structured step-by-step procedures in a code-like format. |
| Open Source Code | Yes | 1Code is available at https://github.com/lmb-freiburg/ucbm. |
| Open Datasets | Yes | The CBMs are evaluated on Image Net (Deng et al., 2009) with a pretrained Res Net-50 V2 (He et al., 2016), CUB (Wah et al., 2011) with Res Net-18 pretrained on CUB, and Places-365 (Zhou et al., 2017) with Res Net-18 pretrained on Places-365. |
| Dataset Splits | Yes | We report top-1 accuracy on the standard holdout sets throughout our experiments. |
| Hardware Specification | Yes | All models were trained on a single NVIDIA RTX 2080 GPU and a full training run took from few minutes to a maximum of 1 2 days depending on dataset size and number of concepts |C|. |
| Software Dependencies | No | We trained our UCBMs with Adam (Kingma & Ba, 2015) and cosine annealing learning rate scheduling (Loshchilov & Hutter, 2017) for 20 epochs. Models are provided at https://github.com/pytorch/vision (Image Net), https://github.com/osmr/imgclsmob (CUB), and https://github.com/Trustworthy-ML-Lab/Label-free-CBM (Places-365). While PyTorch is mentioned for external models, its version specific to the authors' implementation is not provided, nor are versions for other libraries. |
| Experiment Setup | Yes | We trained our UCBMs with Adam (Kingma & Ba, 2015) and cosine annealing learning rate scheduling (Loshchilov & Hutter, 2017) for 20 epochs. We used a learning rate of 0.001 on Image Net and Places-365, and 0.01 on CUB; except for the Jump Re LU for which we set it to 0.08 on CUB. We set α = 0.99 for the elastic net regularization for all variants. We tuned the other hyperparameters (λπ or k, λw, and dropout rate) to yield a good trade-off between performance, sparsity, and fair comparability. Refer to Appendix D for the hyperparameters and to Figure 6 and Appendix G for their effect. |