Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization
Authors: Yanan Wu, Zhixiang Chi, Yang Wang, Konstantinos N. Plataniotis, Songhe Feng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method outperforms the prior works on five WILDS real-world domain shift datasets. Our method can also be integrated with methods with label-dependent optimization to further push the performance boundary. Our code is available at https://github.com/ynanwu/MABN. |
| Researcher Affiliation | Academia | Yanan Wu1,2*, Zhixiang Chi3*, Yang Wang4, Konstantinos N. Plataniotis3, Songhe Feng1,2 1Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing Jiaotong University, Beijing, 100044, China 2School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China 3The Edward S Rogers Sr. ECE Department, University of Toronto, Toronto, M5S3G8, Canada 4Department of Computer Science and Software Engineering, Concordia University, Montreal, H3G2J1, Canada |
| Pseudocode | Yes | Algorithm 1: Meta-auxiliary training of MABN |
| Open Source Code | Yes | Our code is available at https://github.com/ynanwu/MABN. |
| Open Datasets | Yes | In this work, follow Meta-DMo E (Zhong et al. 2022) to evaluate our method on five benchmarks from WILDS (Koh et al. 2021): i Wild Cam (Beery et al. 2021), Camelyon17 (Bandi et al. 2018), Rx Rx1 (Taylor et al. 2019), FMo W (Christie et al. 2018) and Poverty Map (Yeh et al. 2020). |
| Dataset Splits | Yes | Note, that we follow the official training/validation/testing splits, and report the same metrics as in (Koh et al. 2021) |
| Hardware Specification | No | The paper mentions 'computational cost' and model architecture (e.g., ResNet50, DenseNet121, ResNet18) but does not provide specific details on the hardware (GPU, CPU, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'ImageNet-1K pre-trained weights', 'Adam optimizer', and specific models (ResNet50, DenseNet121, ResNet18, BYOL), but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We follow (Zhong et al. 2022) to use Image Net-1K (Deng et al. 2009) pre-trained weights as the initialization to perform joint training. Adam optimizer is used to minimize Eq. 2 with a learning rate (LR) of 1e 4 for 20 epochs. LR is reduced by a factor of 2 when the loss reaches a plateau. λ in Eq. 2 is set to 0.1. During metaauxiliary training, we fix the weight matrix of the entire network, and directly use the running statistics µ and σ for the BN layers. Only the affine parameters γ and β of the BN layers are further optimized using Alg. 1 for 10 epochs with fixed LR of 3e 4 for α and 3e 5 for δ. During testing, for each target domain, we randomly sample 12 images for i Wild Cam and 32 images for the rest datasets to perform adaptation first (Line 12-13 of Alg. 1). The adapted model is then used to test all the images in that domain. The same process is repeated for all target domains. All the experiments are conducted with 5 random seeds to show the variation. |