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..
Learning Unforgotten Domain-Invariant Representations for Online Unsupervised Domain Adaptation
Authors: Cheng Feng, Chaoliang Zhong, Jie Wang, Ying Zhang, Jun Sun, Yasuto Yokota
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct experiments on a wide range of real-world datasets. We modify three offline UDA algorithms, i.e., DANN [Ganin et al., 2016], CDAN [Long et al., 2017], and MCC [Jin et al., 2020], and evaluate their performance on OUDA tasks. |
| Researcher Affiliation | Industry | 1Fujitsu R&D Center, Co., LTD 2Fujitsu LTD |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available in https://github.com/Fujitsu Research/sourcefeature-distillation. |
| Open Datasets | Yes | As the most prevalent benchmark dataset in DA tasks, we conduct experiments on Office-Home [Venkateswara et al., 2017], Office-31 [Saenko et al., 2010] and Image CLEF-DA [Long et al., 2017]. |
| Dataset Splits | No | The whole target domain is divided into a sequential of sub-domains with a batch size of 36 by randomly selecting. We follow the settings in the literature for online training [Kirkpatrick et al., 2017; Mc Mahan et al., 2013] where the algorithms have no access to the target data arrived in previous steps. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | We start OUDA tasks with a source-only (SO) model which trains from scratch with Res Net-50 [He et al., 2016] implemented by Pytorch. The paper mentions 'Pytorch' but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | The training epoch for each step is set as 20. We adopt mini-batch SGD with a momentum of 0.9 and the learning rate annealing strategy as [Ganin et al., 2016]. For all tasks, we use the same hyper-parameter where α = 1 |