Selective Label Enhancement Learning for Test-Time Adaptation
Authors: Yihao Hu, Congyu Qiao, Xin Geng, Ning Xu
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various benchmark datasets validate the effectiveness of the proposed approach. The source code is available at https://github.com/palm-ml/PASLE. We employ four domain generalization datasets including PACS (Li et al., 2017a), VLCS (Torralba & Efros, 2011), Office Home (Venkateswara et al., 2017), and Domain Net (Peng et al., 2019). |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Southeast University, Nanjing, China 2 Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China EMAIL |
| Pseudocode | Yes | Algorithm 1 PASLE Algorithm |
| Open Source Code | Yes | The source code is available at https://github.com/palm-ml/PASLE. |
| Open Datasets | Yes | We employ four domain generalization datasets including PACS (Li et al., 2017a), VLCS (Torralba & Efros, 2011), Office Home (Venkateswara et al., 2017), and Domain Net (Peng et al., 2019). Additionally, we employ two image corruption datasets: CIFAR-10-C and CIFAR-100-C (Hendrycks & Dietterich, 2019). Both datasets introduce 15 types of common image corruptions, categorized into four types: noise, blur, weather, and digital, to the test sets of CIFAR-10 and CIFAR-100 (Krizhevsky, 2009). |
| Dataset Splits | Yes | For source training, we designate one domain as the target and use the remaining domains as source domains. We allocated 20% of the data from the source domains for validation purposes. We use the training sets of CIFAR-10 and CIFAR-100 as source domains and the highest level of corruption in CIFAR-10-C and CIFAR-100-C as target domains. |
| Hardware Specification | Yes | experiments were carried out on the clipart domain of the Domain Net dataset, using Res Net-18 as the backbone with a batch size of 128 on an NVIDIA TITAN Xp GPU. |
| Software Dependencies | No | The paper mentions using Res Net-18 and Res Net-50 models, Adam optimizer, ImageNet-1K pre-trained models, and batch normalization, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For source training, the models are trained using the Adam optimizer with a learning rate of 5e 5 for domain generalization benchmarks and 1e 3 for image corruption benchmarks. All weights are initialized from Image Net-1K (Russakovsky et2 al., 2015) pre-trained models. During testing, we also utilize the Adam optimizer to update all trainable layers without the need for a specific selection. The batch size for the online target domain data is set to 128, with the buffer capacity K set to one-fourth of the batch size, i.e., 32. The learning rate is selected from the range between 1e 3 and 1e 6. The value of τstart is determined by the number of classes in each dataset: for example, VLCS contains 5 classes, while Domain Net has 345 classes, leading to different τstart values for each dataset. The threshold gap, represented as |τstart τend|, is consistently set at 0.1. Furthermore, τdes is uniformly set to 1e 3 for all datasets, except for the large-scale dataset Domain Net, where it is adjusted to 1e 4. |