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..
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
Authors: Shreyash Arya, Sukrut Rao, Moritz Böhle, Bernt Schiele
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a thorough study of design choices to perform this conversion, both for convolutional neural networks and vision transformers. We find that B-cosification can yield models that are on par with B-cos models trained from scratch in terms of interpretability, while often outperforming them in terms of classification performance at a fraction of the training cost. |
| Researcher Affiliation | Collaboration | 1Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany 2RTG Neuroexplicit Models of Language, Vision, and Action, Saarbrücken, Germany 3 Kyutai, Paris, France |
| Pseudocode | No | The paper describes architectural modifications and strategies but does not provide any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release our code and pre-trained model weights at https://github.com/shrebox/B-cosification. |
| Open Datasets | Yes | We B-cosify models from Torchvision [54] supervised on Image Net [16]. |
| Dataset Splits | Yes | Table 4 reports the top-1 classification accuracy on the Image Net validation set... |
| Hardware Specification | Yes | We use NVIDIA A100-SXM4-40GB and Quadro RTX 8000 GPUs from internal cluster. |
| Software Dependencies | No | While software like PyTorch, Captum, and Adam W are mentioned, specific version numbers (e.g., "PyTorch 1.9") are not provided for any of the software components. |
| Experiment Setup | Yes | We optimize using Adam W [26] with cosine scheduling and train for 90 epochs |