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..
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment
Authors: Yin Zhao, minquan wang, Longjun Cai
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By applying the virtual mirror and mirror loss to the generic unsupervised domain adaptation model, we achieved consistently superior performance on several mainstream benchmarks. |
| Researcher Affiliation | Industry | Yin Zhao Alibaba Group EMAIL Minquan Wang Alibaba Group EMAIL Longjun Cai Alibaba Group EMAIL |
| Pseudocode | Yes | The detailed algorithm can be found in Appendix. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is open or publicly available. |
| Open Datasets | Yes | Datasets. We use Office-31 [42], Office-Home[51], Image CLEF and Vis DA2017[39] to validate our proposed method. |
| Dataset Splits | No | The paper mentions using 'validation' as a target domain for VisDA2017, but it does not provide explicit percentages or sample counts for training, validation, or test splits for any of the datasets used in the main text. |
| Hardware Specification | Yes | All the experiments are carried out on one Tesla V100 GPU. |
| Software Dependencies | No | We implement our model in Py Torch. (No version specified for PyTorch or other libraries) |
| Experiment Setup | Yes | The learning rate is adjusted by ηp = η0(1 + αp) β like [17], where p is the epoch which is normalized in [0, 1], η0 = 0.001, α = 10 and β = 0.75. The learning rate of fully connected layers is 10 times of the backbone layers. [...] (K = 3) [...] λ = 1.0. |