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..
LaVAN: Localized and Visible Adversarial Noise
Authors: Danny Karmon, Daniel Zoran, Yoav Goldberg
ICML 2018 | Venue PDF | 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 |