Improving VAEs' Robustness to Adversarial Attack
Authors: Matthew JF Willetts, Alexander Camuto, Tom Rainforth, S Roberts, Christopher C Holmes
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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. |