Editing a classifier by rewriting its prediction rules
Authors: Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. Our approach requires virtually no additional data collection and can be applied to a variety of settings, including adapting a model to new environments, and modifying it to ignore spurious features. |
| Researcher Affiliation | Academia | Shibani Santurkar MIT shibani@mit.edu Dimitris Tsipras MIT tsipras@mit.edu Mahalaxmi Elango MIT melango@mit.edu David Bau MIT davidbau@mit.edu Antonio Torralba MIT torralba@mit.edu Aleksander M adry MIT madry@mit.edu |
| Pseudocode | No | The paper describes a process but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | 1Our code is available at https://github.com/Madry Lab/Editing Classifiers. |
| Open Datasets | Yes | Image Net [Deng et al., 2009, Russakovsky et al., 2015] and Places-365 [Zhou et al., 2017] datasets (cf. Appendix A.2). ... MS-COCO [Lin et al., 2014] and LVIS [Gupta et al., 2019] |
| Dataset Splits | Yes | All other transformed images containing the concept, including those from classes other than the target one, are used for validation and testing (30-70 split). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions models like VGG, ResNet, and CLIP, but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | In each case, we select the best hyperparameters including the choice of the layer to modify based on the validation set performance (cf. Appendix A.6.3). ...Here, hyperparameters (cf. Appendix Table 2) are chosen based on the large-scale synthetic study in Section 4.1. |