Labeling Neural Representations with Inverse Recognition
Authors: Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina Höhne
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the proposed approach across various datasets and models, and illustrate its practical use through multiple examples. |
| Researcher Affiliation | Academia | Kirill Bykov UMI Lab ATB Potsdam Potsdam, Germany kbykov@atb-potsdam.de Laura Kopf UMI Lab ATB Potsdam Potsdam, Germany lkopf@atb-potsdam.de Shinichi Nakajima Machine Learning Group TU Berlin Berlin, Germany nakajima@tu-berlin.de Marius Kloft Machine Learning Group RPTU Kaiserslautern-Landau Kaiserslautern, Germany kloft@cs.uni-kl.de Marina M.-C. Höhne UMI Lab ATB Potsdam University of Potsdam, Germany mhoehne@atb-potsdam.de |
| Pseudocode | No | Appendix A.3 'INVERT algorithm' describes the steps of the algorithm in paragraph form and numbered lists, but it does not present it as a formal pseudocode block or algorithm figure. |
| Open Source Code | Yes | 2The code can be accessed via the following link: https://github.com/lapalap/invert. |
| Open Datasets | Yes | For this, we employed the validation set of Image Net2012 [52] as the dataset DI in the INVERT process. |
| Dataset Splits | Yes | For this, we employed the validation set of Image Net2012 [52] as the dataset DI in the INVERT process. |
| Hardware Specification | Yes | Figure 5 showcases the running time of applying INVERT and Compositional Explanations for explaining 2048 neurons in layer 4 of the FCOS-Res Net50-FPN model [65] pre-trained on the MS COCO dataset [60] on a singe Tesla V100S-PCIE-32GB GPU. |
| Software Dependencies | No | The paper mentions using 'Py Torch models' (e.g., 'Res Net18 and Dense Net161 Py Torch models') but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Parameters of the proposed method include predetermined length L N, the beam size B N. Additionally, during the search process explanations could be constrained to the condition T(ϕ(C)) [α, β], where 0 α < β 0.5. In our experiments, unless otherwise specified, the parameter β is set to 0.5. |