Adversarial Self-Defense for Cycle-Consistent GANs
Authors: Dina Bashkirova, Ben Usman, Kate Saenko
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a quantitative evaluation of the proposed techniques and show that making the translation model more robust to the self-adversarial attack increases its generation quality and reconstruction reliability and makes the model less sensitive to low-amplitude perturbations. Our project page can be found at ai.bu.edu/selfadv/. 5 Experiments and results |
| Researcher Affiliation | Collaboration | Dina Bashkirova 1, Ben Usman1, and Kate Saenko 1,2 1Boston University 2MIT-IBM Watson AI Lab |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our project page can be found at ai.bu.edu/selfadv/. This is a project page, not a direct link to a source code repository or an explicit statement about code availability. |
| Open Datasets | Yes | To provide empirical evidence of our claims, we performed a sequence of experiments on three publicly available image-to-image translation datasets. ... Google Aerial Photo to Maps dataset consisting of 3292 pairs of aerial photos and corresponding maps. ... The dataset is available at [6]. ... Playing for Data (GTA)[26] dataset ... Syn Action [28] synthetic human action dataset |
| Dataset Splits | No | We used 1098 images for training and 1096 images for testing. ... 7500 images for training, 2500 images for testing ... 1561 images in each domain for training 357 images for testing. The paper specifies training and testing splits but does not explicitly mention a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The details on model architectures and choice of hyperparameters used in our experiments can be found in the supplementary materials. The main paper text does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The details on model architectures and choice of hyperparameters used in our experiments can be found in the supplementary materials. The main paper text does not provide specific experimental setup details like hyperparameter values or training configurations. |