A Hypergradient Approach to Robust Regression without Correspondence
Authors: Yujia Xie, Yixiu Mao, Simiao Zuo, Hongteng Xu, Xiaojing Ye, Tuo Zhao, Hongyuan Zha
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough numerical experiments show that ROBOT achieves better performance than existing methods in both linear and nonlinear regression tasks, including real-world applications such as flow cytometry and multi-object tracking. |
| Researcher Affiliation | Academia | Yujia Xie, Simiao Zuo, Tuo Zhao are affiliated with Georgia Institute of Technology. Emails: {xieyujia, simiaozuo, tourzhao}@gatech.edu. Yixiu Mao is affiliated with Shanghai Jiao Tong University. Email: 956986044myx@gmail.com. Hongteng Xu is affiliated with Gaoling School of Artificial Intelligence, Renmin University of China, and Beijing Key Laboratory of of Big Data Management and Analysis Methods. Email: hongtengxu@ruc.edu.cn. Xiaojing Ye is affiliated with Georgia State University. Email: xye@gsu.edu. Hongyuan Zha is affiliated with School of Data Science, Shenzhen Institute of Artificial Intelligence and Robotics for Society, the Chinese University of Hong Kong, Shenzhen. Email: zhahy@cuhk.edu.cn. |
| Pseudocode | Yes | Algorithm 1 Solving S r for robust matching |
| Open Source Code | No | The paper mentions obtaining initial weights for a third-party model (Siam RPN) from a GitHub repository, but does not state that the code for their own proposed methodology (ROBOT) is openly available. |
| Open Datasets | Yes | For RWOC (i.e., exact correspondence), we use several synthetic regression datasets and a real gated flow cytometry dataset... We adopt the dataset from Knight et al. (2009)... We adopt the MOT17 (Milan et al., 2016) and the MOT20 (Dendorfer et al., 2020) datasets. |
| Dataset Splits | Yes | We use 90% of the data as the training data, and the remaining as test data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Here, AM and ROBOT is trained with batch size 500 and learning rate 10 4 for 2, 000 iterations. For the Sinkhorn algorithm in ROBOT we set ϵ = 10 4. |