Evolving Standardization for Continual Domain Generalization over Temporal Drift
Authors: Mixue Xie, Shuang Li, Longhui Yuan, Chi Liu, Zehui Dai
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple real-world datasets including images and texts validate the efficacy of our Evo S. |
| Researcher Affiliation | Collaboration | Mixue Xie1 Shuang Li1, Longhui Yuan1 Chi Harold Liu1 Zehui Dai2 1Beijing Institute of Technology, China 2Lazada Search & Monetisation Tech, China |
| Pseudocode | Yes | Algorithm 1: Training procedure for Evo S" and "Algorithm 2: Inference procedure for Evo S" are provided in Appendix C. |
| Open Source Code | Yes | Code is available at https://github.com/BIT-DA/Evo S. |
| Open Datasets | Yes | Thanks to the work in [56], several real-world datasets with distribution shifts over time have been available. And we evaluate Evo S on three image classification datasets (Yearbook and f Mo W from [56] and RMNIST) and two text classification datasets (Huffpost and Arxiv from [56]). |
| Dataset Splits | Yes | For each training domain of all datasets, we randomly select 90% data as training split and 10% data as validation split. |
| Hardware Specification | Yes | We run each task on a single NVIDIA Ge Force RTX 3090 GPU for three random trials. |
| Software Dependencies | No | The paper mentions 'All experiments are implemented via Py Torch' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For optimization, we use the Adam optimizer with lr = 1e 3 for Yearbook and RMNIST, lr = 2e 4 for f Mo W and lr = 2e 5 for Huffpost and Arxiv. The batch size is set to 64 for all datasets. As for hyper-parameters, we select them via grid search using the validation splits of training domains and finally use α = 2.0 for RMNIST, α = 1.0 for others, λ = 1.0, W = 3 for all datasets. |