A Reparametrization-Invariant Sharpness Measure Based on Information Geometry
Authors: Cheongjae Jang, Sungyoon Lee, Frank Park, Yung-Kyun Noh
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now apply our sharpness measure as a regularizer to train neural networks to see if regularizing the measure can improve the generalization performance. The corresponding loss function is written as... We consider a toy example and then consider image classification tasks involving MNIST and CIFAR-10/100 data sets. ... Table 1: Averages and standard errors of the test classification accuracies for SGD, GR, SAM, ASAM, and SGD with our regularization method on MNIST, CIFAR-10, and CIFAR-100 data sets. |
| Researcher Affiliation | Collaboration | Cheongjae Jang Hanyang University cjjang@hanyang.ac.kr Sungyoon Lee Korea Institute for Advanced Study sungyoonlee@kias.re.kr Frank C. Park Seoul National University Saige Research fcp@snu.ac.kr Yung-Kyun Noh Hanyang University Korea Institute for Advanced Study nohyung@hanyang.ac.kr |
| Pseudocode | No | The paper describes methods and computations but does not include a clearly labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | Yes | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See supplemental materials. |
| Open Datasets | Yes | We consider a toy example and then consider image classification tasks involving MNIST and CIFAR-10/100 data sets. |
| Dataset Splits | No | No explicit percentages or sample counts for training/validation/test splits are provided in the main paper. It mentions 'Train Test Poison' in figures, but no specific split details. |
| Hardware Specification | No | The paper states 'See Appendix G.4.2.' for hardware specifications, but Appendix G.4.2 is not provided in the given text. |
| Software Dependencies | No | All the experiments have been performed using the Py Torch library [38]. (No version specified) |
| Experiment Setup | No | For this experiment, we regularize the mini-batch IGS defined in (7)... The detailed experimental settings are explained in Appendix G.4.2. |