B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
Authors: Shreyash Arya, Sukrut Rao, Moritz Böhle, Bernt Schiele
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 |