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..
Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation
Authors: Pin Jiang, Aming Wu, Yahong Han, Yunfeng Shao, Meiyu Qi, Bingshuai Li
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the effectiveness of Bi AT on three benchmark datasets and experimental results demonstrate the proposed method achieves the state-of-the-art. We evaluate Bi AT on three benchmark datasets and conduct extensive ablation study. |
| Researcher Affiliation | Collaboration | Pin Jiang1,2 , Aming Wu1,2 , Yahong Han1,2 , Yunfeng Shao3 , Meiyu Qi3 and Bingshuai Li3 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin, China 3Huawei Noah s Ark Lab EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Bidirectional Adversarial Training (Bi AT) |
| Open Source Code | No | The paper does not provide any explicit statement about making source code available or a link to a code repository. |
| Open Datasets | Yes | Domain Net is a recent benchmark dataset for large-scale domain adaptation that has 6 domains and 345 classes [Peng et al., 2019]. Office contains 3 domains with 31 classes and we eliminate the domains with less examples and construct 2 scenarios, Webcam to Amazon (W A) and DSLR to Amazon (D A). Office-Home contains 4 domains (Real, Clipart, Art, Product) with 65 classes. |
| Dataset Splits | Yes | For each dataset, we use the randomly selected one or three labeled examples per class as the labeled target examples (1shot and 3-shot) by [Saito et al., 2019]. Other three labeled target examples are used as validation set and the remaining are used as unlabeled target data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Alex Net and Res Net34 as backbones, and SGD as an optimizer, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set λ1 in Eq. (11) as 1, λ2 as 2. To avoid misjudgments of Le-vat in early training, we use cosine warm-up on λ3. Lmme is sensitive to datasets, even sub-domains of a same dataset, and we choose λ4 [0.1, 2.5] for different scenarios. We adopt SGD as an optimizer with initial learning rate 0.01. In order to make a fair comparison, other setup we choose the same as [Saito et al., 2019]. |