LaVAN: Localized and Visible Adversarial Noise
Authors: Danny Karmon, Daniel Zoran, Yoav Goldberg
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment with two setups: working in the Network Domain and in the Image Domain. 2.2. Experiments and Results We use the Py Torch provided pre-trained Inception V3 network (Szegedy et al., 2016) trained on Image Net. |
| Researcher Affiliation | Collaboration | 1Microsoft, Herzliya, Israel. 2Deep Mind, London, UK. 3Department of Computer Science, Bar-Ilan University,Ramat Gan, Israel. |
| Pseudocode | Yes | Algorithm 1 Localized Noising Process |
| Open Source Code | No | The paper does not provide any specific repository links or explicit statements about releasing the source code for the described methodology. |
| Open Datasets | Yes | We use the Py Torch provided pre-trained Inception V3 network (Szegedy et al., 2016) trained on Image Net. |
| Dataset Splits | No | The paper mentions a “training set of 100 images” and refers to a “separate test set, consisting of 100 images”, but does not provide specific details for validation dataset splits (e.g., percentages, counts, or explicit use of a validation set for hyperparameter tuning) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions “Py Torch” and “Inception V3 network” but does not specify exact version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We use images of size 299x299 and noise a square patch of size 42x42, roughly 2% of the image pixels. For each image, we train the noise until we reach the desired confidence, or up to 10,000 iterations. Target class probability 0.9 |