Representation Learning with Multiple Lipschitz-Constrained Alignments on Partially-Labeled Cross-Domain Data
Authors: Songlei Jian, Liang Hu, Longbing Cao, Kai Lu4320-4327
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | MULAN shows its superior performance on partially-labeled semisupervised domain adaptation and few-shot domain adaptation and outperforms the state-of-the-art visual domain adaptation models by up to 12.1%. Experiments Experimental Setup Datasets and Evaluation Traditional visual DA datasets, such as MNIST, USPS, and SVHN, have been reported that they are over-evaluated to achieve very high accuracy for almost all recent models (Tzeng et al. 2017; Motiian et al. 2017). Therefore, we adopt the latest Vis DA Challenge dataset (Peng et al. 2017) in our experiments... |
| Researcher Affiliation | Academia | Songlei Jian, Liang Hu, Longbing Cao, Kai Lu College of Computer, National University of Defense Technology, China Advanced Analytics Institute, University of Technology Sydney, Australia {jiansonglei, kailu}@nudt.edu.cn, rainmilk@gmail.com, longbing.cao@uts.edu.au |
| Pseudocode | Yes | Algorithm 1 The Learning Process of MULAN |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | Therefore, we adopt the latest Vis DA Challenge dataset (Peng et al. 2017) in our experiments, which supports object classification of syntheticand real-object images. |
| Dataset Splits | Yes | The classification accuracy (mean std%) of Synthetic Real domain adaptation with 5-fold validation on the Vis DA Challenge dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions the use of 'ResNet50 features'. |
| Software Dependencies | No | The paper mentions implementing 1-Lipschitz functions with SNMLPs and using Adam optimizer, and ResNet50 features, but does not provide specific version numbers for any software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | We set a small margin mε = 1e 3 in Eqn. 9 to avoid overfitting to the target representation space Ht." and "we set γ = 0.02 in this paper through empirical test" and "All the image features, i.e., Xt and Xs in these methods are represented by Res Net50 features (He et al. 2016) that are pre-trained on Image Net." |