Cross-Domain Transferability of Adversarial Perturbations
Authors: Muhammad Muzammal Naseer, Salman H. Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, Fatih Porikli
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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. |