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..
Adversarial Robustness in Multi-Task Learning: Promises and Illusions
Authors: Salah Ghamizi, Maxime Cordy, Mike Papadakis, Yves Le Traon697-705
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we evaluate the design choices that impact the robustness of multi-task deep learning networks. We provide evidence that blindly adding auxiliary tasks, or weighing the tasks provides a false sense of robustness. ... Surprisingly, our experimental study shows that adding more tasks does not consistently increase robustness, and may even have negative effects. |
| Researcher Affiliation | Academia | Salah Ghamizi, Maxime Cordy, Mike Papadakis, and Yves Le Traon University of Luxembourg EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | We describe full algorithm in Appendix D. |
| Open Source Code | Yes | We provide the appendix, all our algorithms, models, and open source-code at https://github.com/yamizi/taskaugment |
| Open Datasets | Yes | We use the Taskonomy dataset, an established dataset for multi-task learning (Zamir et al. 2018). |
| Dataset Splits | Yes | We use the architectures and training settings of the original Taskonomy paper (Zamir et al. 2018) |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. It only mentions 'Resnet18 encoder' and 'Xception, Wide-Resnet, and Resnet' which are model architectures. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used for the experiments. |
| Experiment Setup | Yes | We use as base setting the l Projected Gradient Descent attack (PGD) (Madry et al. 2017) with 25 steps attacks, a strength of ϵ = 8/255 and a step size α = 2/255. ... We use a uniform weights, Cross-entropy loss for the semantic segmentation task and an L1 loss for the other tasks. |