Topology-aware Robust Optimization for Out-of-Distribution Generalization
Authors: Fengchun Qiao, Xi Peng
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate TRO in a wide range of tasks including classification, regression, and semantic segmentation. We compare TRO with SOTA baselines on OOD generalization and conduct ablation study on the key components of TRO. |
| Researcher Affiliation | Academia | Fengchun Qiao University of Delaware fengchun@udel.edu Xi Peng University of Delaware xipeng@udel.edu |
| Pseudocode | Yes | Algorithm 1: TRO Algorithm |
| Open Source Code | Yes | The source code and pre-trained models are available at: https://github.com/joffery/TRO. |
| Open Datasets | Yes | DG-15 (Xu et al., 2022) is a synthetic binary classification dataset... TPT-48 (Vose et al., 2014) contains the monthly average temperature... Sen1Floods11 (Bonafilia et al., 2020) is a public dataset for flood mapping at the global scale... we have conducted experiments on PACS (Li et al., 2017), Terra (Beery et al., 2018), and VLCS (Fang et al., 2013). |
| Dataset Splits | Yes | Following Gulrajani & Lopez-Paz (2021), we perform model selection based on a validation set constructed from training groups only... data of other events are split into training and validation sets with a random 80-20 split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions using default values from other implementations, such as 'default values from the official implementation1 of Tong et al. (2021)' and 'default values from Xu et al. (2022) (DG-15/-60 and TPT-48) and Bonafilia et al. (2020) (Sen1Floods11)'. However, it does not explicitly list specific software or library versions like Python, PyTorch, or TensorFlow versions. |
| Experiment Setup | Yes | We select λ from {1e-3, 1e-2, 1e-1, 1, 10, 100} and select ηq from {1e-4, 1e-3, 1e-2, 1e-1, 1}. |