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 | Conference PDF | Archive PDF | Plain Text | 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 |