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..
Improving VAEs' Robustness to Adversarial Attack
Authors: Matthew JF Willetts, Alexander Camuto, Tom Rainforth, S Roberts, Christopher C Holmes
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We confirm their capabilities on several different datasets and with current state of the art VAE adversarial attacks, and also show that they increase the robustness of downstream tasks to attack. |
| Researcher Affiliation | Academia | 1University of Oxford 2Alan Turing Institute, London |
| Pseudocode | No | The paper contains mathematical derivations and proofs, but no structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We carry our these attacks for d Sprites (Matthey et al., 2017), Chairs (Aubry et al., 2014) and 3D faces (Paysan et al., 2009), for a range of β and λ values. |
| Dataset Splits | No | The paper does not explicitly state the training, validation, and test dataset splits (e.g., percentages or sample counts) for the datasets used. |
| Hardware Specification | No | All runs were done on the Azure cloud system on NC6 GPU machines. |
| Software Dependencies | No | The paper mentions using ADAM for training but does not provide specific version numbers for any software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | To train the model we used ADAM Kingma & Lei Ba (2015) with default parameters, a cosine decaying learning rate of 0.001, and a batch size of 1024. |