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..
Towards Robust ResNet: A Small Step but a Giant Leap
Authors: Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan Kankanhalli
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on real-world datasets corroborates our analytical findings that a small h can indeed improve both its training and generalization robustness. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, National University of Singapore 2RIKEN Center for Advanced Intelligence Project 3IBM Research, Singapore |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | In this section, we conduct experiments on the vision-based CIFAR-10 dataset [Krizhevsky and Hinton, 2009] and the text-based AG-NEWS dataset [Zhang et al., 2015]. |
| Dataset Splits | No | The paper does not explicitly provide details about a validation dataset split (e.g., percentages or sample counts for a validation set). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions). |
| Experiment Setup | Yes | Unless specified otherwise, the default optimizer is SGD with 0.9 momentum. We train a Res Net using the CIFAR-10 dataset for 80 epochs with an initial learning rate (LR) of 0.1 that is divided by 10 at epochs 40 and 60. We train another Res Net using the AG-NEWS dataset with a fixed LR of 0.1 for 15 epochs. |