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..
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
Authors: Hassan Dbouk, Naresh Shanbhag
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of GDWS via extensive experiments on CIFAR-10, SVHN, and Image Net datasets. |
| Researcher Affiliation | Academia | Hassan Dbouk & Naresh R. Shanbhag Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801 EMAIL |
| Pseudocode | Yes | Algorithm 1: (MEGO) Minimum Error Complexity-constrained GDWS Optimal Approximation Algorithm 2: (LEGO) Least Complex Errorconstrained GDWS Optimal Approximation Algorithm 3: Constructing GDWS networks |
| Open Source Code | Yes | Our code can be found at https://github.com/hsndbk4/ GDWS. |
| Open Datasets | Yes | We demonstrate the effectiveness of GDWS via extensive experiments on CIFAR-10, SVHN, and Image Net datasets. |
| Dataset Splits | No | The paper mentions training and testing, and refers to an appendix for "Details on the training/evaluation setup", but does not explicitly provide training/validation/test dataset splits in the main text. |
| Hardware Specification | Yes | We measure the throughput in FPS by mapping the networks onto an NVIDIA Jetson Xavier via native Py Torch [24] commands... (a) robust accuracy against ℓ -bounded perturbations vs frames-per-second measured on an NVIDIA Jetson Xavier, and (b) total time required to implement these methods measured on a single NVIDIA 1080 Ti GPU. |
| Software Dependencies | No | The paper mentions using "Py Torch [24]" but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We report Arob against ℓ bounded perturbations generated via PGD [21] with standard attack strengths: ϵ = 8/255 with PGD-100 for both CIFAR-10 [17] and SVHN [23] datasets, and ϵ = 4/255 with PGD-50 for the Image Net [29] dataset. |