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 [1].
Disjoint Label Space Transfer Learning with Common Factorised Space
Authors: Xiaobin Chang, Yongxin Yang, Tao Xiang, Timothy M. Hospedales3288-3295
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments The proposed model is evaluated on progressively more challenging problems. First, we evaluate CFSM on unsupervised domain adaptation (UDA). Second, different DLSTL settings are considered, including semi-supervised DLSTL classification and unsupervised DLSTL retrieval. CFSM handles all these scenarios with minor modifications. The effectiveness CFSM is demonstrated by its superior performance compared to the existing work. Finally insight is provided through ablation study and visualisation analysis. |
| Researcher Affiliation | Academia | 1Queen Mary University of London, 2The University of Edinburgh |
| Pseudocode | No | The paper describes the model architecture and optimization process but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | SVHN (Netzer et al. 2011) is the labelled source dataset and MNIST (Le Cun et al. 1998) is the unlabelled target. ... Market (Zheng et al. 2015) and Duke (Zheng, Zheng, and Yang 2017). ... Sketchy dataset (Sangkloy et al. 2016). |
| Dataset Splits | Yes | Results are averaged over ten random splits as in (Luo et al. 2017). ... We randomly split 75 classes as a labelled source domain and use the remaining 50 classes to define an unlabelled target domain with disjoint label space. |
| Hardware Specification | No | The paper mentions using |
| Software Dependencies | No | The paper mentions "Adam optimiser" but does not specify its version or the versions of any other software dependencies. |
| Experiment Setup | Yes | We set d C = 50, βM = 0.001 and βC = 0.01. ... We set d C = 10, βM = βC = 0.01. The learning rate is 0.001 and the Adam (Kingma and Ba 2014) optimiser is used. ... We set d C = 2048, βM = 2.0, βC = 0.01. Adam optimiser is used with learning rate 3.5e 4. ... We set d C = 512, βM = 10 3, βC = 0.1. Adam optimiser with learning rate 10 4 is used. |