Efficient and Consistent Adversarial Bipartite Matching
Authors: Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our approach, we apply our adversarial bipartite matching model to video tracking tasks using public benchmark datasets (Leal-Taix e et al., 2015). Our future work will explore matching problems with different loss functions and other graphical structures. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Illinois at Chicago. |
| Pseudocode | Yes | Algorithm 1 Double Oracle Algorithm for Adversarial Bipartite Matching Equilibria. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the availability of its source code. |
| Open Datasets | Yes | To evaluate our approach, we apply our adversarial bipartite matching model to video tracking tasks using public benchmark datasets (Leal-Taix e et al., 2015). Table 3. Dataset properties DATASET # ELEMENTS # EXAMPLES TUD-CAMPUS 12 70 TUD-STADTMITTE 16 178 ETH-SUNNYDAY 18 353 ETH-BAHNHOF 34 999 ETH-PEDCROSS2 30 836 |
| Dataset Splits | Yes | To tune the regularization parameter (λ in adversarial matching, and C in SSVM), we perform 5-fold cross validation based on the training dataset only. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions software like "min Conf" (Schmidt, 2008) and "SVM-Struct" (Joachims, 2008; Vedaldi, 2011) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper mentions tuning regularization parameters but does not provide specific values for hyperparameters such as learning rate, batch size, number of epochs, or optimizer settings. |