Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Domain Generalization through the Lens of Angular Invariance
Authors: Yujie Jin, Xu Chu, Yasha Wang, Wenwu Zhu
IJCAI 2022 | Venue PDF | 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. |