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..
Adversarial Feature Desensitization
Authors: Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake Richards, Irina Rish
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on several benchmarks demonstrate the effectiveness of the proposed approach against a wide range of attack types and attack strengths. |
| Researcher Affiliation | Academia | 1 Mc Gill University, Montreal, Canada 2 MILA, Université de Montréal, Montreal, Canada *Correspondence to: EMAIL |
| Pseudocode | Yes | Algorithm 1: AFD training procedure |
| Open Source Code | Yes | Our code is available at https://github.com/Bashivan Lab/afd. |
| Open Datasets | Yes | Datasets. We validated our proposed method on several common datasets including MNIST [30], CIFAR10, CIFAR100 [29], and tiny-Imagenet [26]. |
| Dataset Splits | Yes | To find the best learning rates, we randomly split the CIFAR10 train set into a train and validation sets (45000 and 5000 images in train and validation sets respectively). |
| Hardware Specification | Yes | All experiments were run on NVIDIA V100 GPUs. We used one GPU for experiments on MNIST and 2 GPUs for other datasets. |
| Software Dependencies | No | The paper mentions using "Foolbox [42] and Advertorch [12] Python packages" but does not specify their version numbers or any other software dependencies with version information. |
| Experiment Setup | Yes | We used ϵ = 0.3, 0.031, and 0.016 for MNIST, CIFAR, and Tiny-Imagenet datasets respectively. ... Based on this analysis, we selected the learning rate γ = 0.5 for tuning the feature extractor Fθ, and α = β = 0.1 for tuning the parameters in domain discriminator Dψ, and the task classifier Cφ. |