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..
Invariant Information Bottleneck for Domain Generalization
Authors: Bo Li, Yifei Shen, Yezhen Wang, Wenzhen Zhu, Colorado Reed, Dongsheng Li, Kurt Keutzer, Han Zhao7399-7407
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we analyze IIB s performance with extensive experiments on both synthetic and large-scale benchmarks. We show that IIB is able to eliminate the spurious information better than other existing DG methods, and achieves consistent improvements on 7 datasets by 0.7% on Domain Bed (Gulrajani and Lopez-Paz 2020). |
| Researcher Affiliation | Collaboration | 1 Microsoft Research Asia, China 2 Hong Kong University of Science and Technology, China 3 Washington University in St. Louis, USA 4 University of California, Berkeley, USA 5 University of Illinois at Urbana-Champaign, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code implementation of IIB is released at Github.1 1https://github.com/Luodian/IIB/tree/IIB |
| Open Datasets | Yes | CS-CMNIST (Ahuja et al. 2021) CS-CMNIST is a tenway classification task. The images are all drawn from MNIST. ... Geometric Skew CIFAR10 (Nagarajan, Andreassen, and Neyshabur 2021) ... conduct experiments on Domain Bed (Gulrajani and Lopez-Paz 2020) with 7 different datasets of different sizes. |
| Dataset Splits | Yes | We split 20% from train set as validation set. ... During training, the validation set is a subset of training set, we choose the model that performs best on the overall validation set for each domain. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions network architectures and training iterations. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | For IIB specific hyper-parameters, we set λ [1,102], and β [10 3,10 4]. For backbone feature extractor, in Rotated/Colored-MNIST, we use 4-layers 3x3 Conv Net. For VLCS and PACS, we use Res Net-18 (He et al. 2016). For larger datasets, we opt to Res Net-50. For classifier, we both test linear and non-linear invariant (environment) classifiers. ... the network is trained for 5000 iterations with batch size set to 128. |