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..
QuadAttac$K$: A Quadratic Programming Approach to Learning Ordered Top-$K$ Adversarial Attacks
Authors: Thomas Paniagua, Ryan Grainger, Tianfu Wu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, the proposed Quad Attac K is tested in the Image Net-1k classification using Res Net-50, Dense Net-121, and Vision Transformers (Vi T-B and DEi T-S).4 Experiments |
| Researcher Affiliation | Academia | Thomas Paniagua, Ryan Grainger and Tianfu Wu Department of Electrical and Computer Engineering, NC State EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Details are provided in the Appendix A and our released code repository. |
| Open Datasets | Yes | In this section, we evaluate our Quad Attac K with K = 1, 5, 10, 15, 20 in the Image Net-1k benchmark [Russakovsky et al., 2015] using two representative pretrained Conv Nets: the Res Net-50 [He et al., 2016] and the Dense Net-121 Huang et al. [2017], and two representative pretrained Transformers: the vanilla Vision Transformer (Base) [Dosovitskiy et al., 2020] and the data-efficient variant DEi T (small) [Touvron et al., 2021]. |
| Dataset Splits | Yes | In Image Net-1k [Russakovsky et al., 2015], there are 50, 000 images for validation. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Py Torch" and "mmpretrain package" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The selection of learning rates γ and λ values (see Eqn. 10) in the context of learning attacks requires careful consideration to achieve the desired trade-offs (see Fig. 3) and optimize the attack performance. If K < 5, the learning rate is set to γ = 0.75e 3 (for all the four models). For our Quad Attac K... we set λ = 0.5 for Quad Attac K and λ = 5 for the logit/probability space losses for the K = 1 case. For all other values of K, we use λ = 10... |