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..
Cross-Domain Transferability of Adversarial Perturbations
Authors: Muhammad Muzammal Naseer, Salman H. Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, Fatih Porikli
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments |
| Researcher Affiliation | Collaboration | 1Australian National University, Canberra, Australia 2Inception Institute of Artificial Intelligence, Abu Dhabi, UAE 3CVL, Department of Electrical Engineering, Linköping University, Sweden |
| Pseudocode | Yes | The overall training scheme for the generative network is given in Algorithm 1. |
| Open Source Code | Yes | Code is available at: https://github.com/Muzammal-Naseer/Cross-domain-perturbations |
| Open Datasets | Yes | Datasets. We consider the following datasets for generator training namely Paintings [29], Comics [30], Image Net and a subset of Chest X-ray (Chest X) [28]. |
| Dataset Splits | Yes | Inference: Inference is performed on Image Net validation set (val-set) (50k samples), a subset (5k samples) of Image Net proposed by [11] and Image Net-Neur IPS [31] (1k samples) dataset. |
| Hardware Specification | No | The paper describes the model architecture, optimizer, and training details, but does not specify the hardware (e.g., GPU models, CPU, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a ResNet architecture and Adam optimizer, but does not specify the versions of software libraries or frameworks (e.g., TensorFlow, PyTorch) used for implementation. |
| Experiment Setup | Yes | For training, we used Adam optimizer [23] with a learning rate of 1e-4 and values of exponential decay rate for first and second moments set to 0.5 and 0.999, respectively. |