Domain Generalization through the Lens of Angular Invariance
Authors: Yujie Jin, Xu Chu, Yasha Wang, Wenwu Zhu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple DG benchmark datasets validate the effectiveness of the proposed AIDGN method. |
| Researcher Affiliation | Academia | 1 Peking University, Beijing, China 2 Tsinghua University, Beijing, China |
| Pseudocode | No | The paper describes its methodology through mathematical derivations and textual explanations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are avalable at https://github.com/Jin Yujie99/aidgn |
| Open Datasets | Yes | We conduct our experiments on four public benchmark datasets to evaluate the effectiveness of the proposed AIDGN. PACS [Li et al., 2017], VLCS [Fang et al., 2013], Office Home [Venkateswara et al., 2017], Terra Incognita [Beery et al., 2018] |
| Dataset Splits | Yes | For training, we randomly split each training domain into 8:2 training/validation splits, choose the model on the overall validation set, and then evaluate its performance on the target domain set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and the Domain Bed framework but does not provide specific version numbers for programming languages, libraries, or other software dependencies. |
| Experiment Setup | Yes | We construct a mini-batch containing all source domains where each domain has 32 images. We freeze all the batch normalization (BN) layers from pre-trained Res Net since different domains in a mini-batch follow different distributions. The network is trained for 5000 iterations using the Adam [Kingma and Ba, 2015] optimizer. To estimate δy,ϕ and µd,y,ϕ in AIDGN loss (16), we treat all δy,ϕ as one single hyperparameter δ, and perform in-batch estimation for the d-th domain norm scale parameter, i.e., µd, while ignoring the index y and ϕ. |