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..
Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
Authors: Qiwei Tian, Chenhao Lin, Zhengyu Zhao, Qian Li, Chao Shen
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three datasets demonstrate that CA-TRIDE outperforms existing defense methods in both conventional and new metrics. |
| Researcher Affiliation | Academia | Qiwei Tian 1 Chenhao Lin 1 Zhengyu Zhao 1 Qian Li 1 Chao Shen 1 1Xi an Jiao Tong University, Xi an, China. Correspondence to: Chenhao Lin <EMAIL>, Chao Shen <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Generating Adversarial Triplets in CA-TRIDE |
| Open Source Code | Yes | Codes are available at https://github.com/michaeltian108/CA-TRIDE. |
| Open Datasets | Yes | Evaluations are on three popular datasets in image retrieval tasks, i.e. CUB-200-2011 (Welinder et al., 2010), Cars196 (Krause et al., 2013), and SOP (Oh Song et al., 2016). |
| Dataset Splits | No | The paper describes a 'semi-hard sampling' strategy for mini-batch sampling and an epoch-wise adjustment to η, but it does not provide explicit train/validation/test dataset splits (e.g., percentages or sample counts) for reproduction. |
| Hardware Specification | Yes | We conducted 5 runs of HM and CA-TRIDE on the CUB dataset with an RTX3090 GPU. |
| Software Dependencies | No | The paper mentions using 'ADAM(Kingma & Ba, 2014) optimizer' and 'PGD (Madry et al., 2017)' but does not specify version numbers for these or any other software components or libraries used. |
| Experiment Setup | Yes | We train our models using ADAM(Kingma & Ba, 2014) optimizer with a 1.0 10 3 learning rate, a mini-batch size of 112, and training epochs of 100 under the above three datasets. For the top-rank pair, γ = 0.5 and the triplet margin in LT R βT R is 0.04. Adversarial perturbation is generated through PGD (Madry et al., 2017) with an optimization step α = 1/255, 16 iterations and clipped by an l norm of ϵ = 8/255. |