Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Attention-based Interpretability with Concept Transformers
Authors: Mattia Rigotti, Christoph Miksovic, Ioana Giurgiu, Thomas Gschwind, Paolo Scotton
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our Concept Transformer module on established explainability benchmarks and show how it can be used to infuse domain knowledge into classifiers to improve accuracy, and conversely to extract concept-based explanations of classification outputs. |
| Researcher Affiliation | Industry | Mattia Rigotti, Christoph Miksovic, Ioana Giurgiu, Thomas Gschwind & Paolo Scotton IBM Research Zurich, Switzerland EMAIL |
| Pseudocode | Yes | C APPENDIX: CONCEPTTRANSFORMER PYTORCH CODE |
| Open Source Code | Yes | Code to reproduce our results is available at: https://github.com/ibm/concept_transformer. |
| Open Datasets | Yes | We validate our approach on three image benchmark datasets, MNIST Even/Odd (Barbiero et al., 2021), CUB-200-2011 (Welinder et al., 2010), and a PY (Farhadi et al., 2009). |
| Dataset Splits | Yes | Figure 2 shows the accuracy on the test set (left) and explanation loss during validation (right), relative to the number of samples used at training, which varies from 100 to 7000. [...] For validation and testing, only resizing and normalization were applied. |
| Hardware Specification | No | The paper does not specify the hardware used for the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | We use the Albumentations library by Buslaev et al. (2020). The paper mentions a software library but does not provide specific version numbers for all key software components (e.g., PyTorch version used in Appendix C). |
| Experiment Setup | Yes | At training time, the following augmentations were applied to the individual object samples: resizing to a standardized format (H W = 320 320 pixels), random horizontal flipping with probability p = 0.5, random rotations in the range of 15 based on an uniform probability distribution and normalization. For validation and testing, only resizing and normalization were applied. |