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..
Direct Parameterization of Lipschitz-Bounded Deep Networks
Authors: Ruigang Wang, Ian Manchester
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A comprehensive set of experiments on image classification shows that sandwich layers outperform previous approaches on both empirical and certified robust accuracy.Our experiments have two goals: First, to illustrate that our model parameterization can provide a tight Lipschitz bounds via a simple curve-fitting tasks. Second, to examine the performance and scalability of the proposed method on robust image classification tasks. |
| Researcher Affiliation | Academia | 1Australian Centre for Robotics, School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia. |
| Pseudocode | Yes | Algorithm 1 1-Lipschitz convolutional layer |
| Open Source Code | Yes | Code is available at https://github.com/acfr/LBDN. |
| Open Datasets | Yes | We conducted a set of empirical robustness experiments on CIFAR-10/100 and Tiny-Imagenet datasets |
| Dataset Splits | No | For the curve fitting experiment, we take 300 and 200 samples (xi, yi) with xi U([ −2, 2]) for training and testing, respectively. However, for the main image classification tasks, no explicit train/validation/test splits (e.g., percentages or counts) are provided in the text. |
| Hardware Specification | Yes | All experiments were performed on an Nvidia A5000. |
| Software Dependencies | No | Pytorch code is available at https://github.com/acfr/LBDN. While PyTorch is mentioned, no specific version number for PyTorch or any other software dependency is provided. |
| Experiment Setup | Yes | For all experiments, we used a piecewise triangular learning rate (Coleman et al., 2017) with maximum rate of 0.01. We use Adam (Kingma & Ba, 2014) and Re LU as our default optimizaer and activation, respectively. ... We use batch size of 50 and Lipschitz bounds of 1, 5 and 10. ... All models are trained with normalized input data for 100 epochs. |