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..
Multi-Adversarial Domain Adaptation
Authors: Zhongyi Pei, Zhangjie Cao, Mingsheng Long, Jianmin Wang
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evidence demonstrates that the proposed model outperforms state of the art methods on standard domain adaptation datasets. |
| Researcher Affiliation | Academia | Zhongyi Pei, Zhangjie Cao, Mingsheng Long, Jianmin Wang KLiss, MOE; NEL-BDS; TNList; School of Software, Tsinghua University, China EMAIL EMAIL |
| Pseudocode | No | The information is insufficient. The paper includes an architecture diagram (Figure 2) but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes, datasets and configurations will be available online at github.com/thuml. |
| Open Datasets | Yes | Office-31 (Saenko et al. 2010) is a standard benchmark for visual domain adaptation... and Image CLEF-DA1 is a benchmark dataset for Image CLEF 2014 domain adaptation challenge... 1http://imageclef.org/2014/adaptation |
| Dataset Splits | Yes | We follow standard evaluation protocols for unsupervised domain adaptation (Long et al. 2015; Ganin and Lempitsky 2015). For both Office-31 and Image CLEF-DA datasets, we use all labeled source examples and all unlabeled target examples. ... We also adopt transfer cross-validation (Zhong et al. 2010) to select parameter λ for the MADA models. |
| Hardware Specification | No | The information is insufficient. The paper does not specify any particular hardware components like GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The information is insufficient. The paper mentions 'Caffe (Jia et al. 2014) framework' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We employ the mini-batch stochastic gradient descent (SGD) with momentum of 0.9 and the learning rate strategy implemented in Rev Grad (Ganin and Lempitsky 2015): the learning rate is not selected by a grid search due to high computational cost it is adjusted during SGD using these formulas: ηp = η0 (1+αp)β , where p is the training progress linearly changing from 0 to 1, η0 = 0.01, α = 10 and β = 0.75... To suppress noisy activations at the early stages of training, instead of fixing parameter λ, we gradually change it by multiplying 2 1+exp( δp) 1, where δ = 10 (Ganin and Lempitsky 2015). |