Implicit Semantic Response Alignment for Partial Domain Adaptation
Authors: Wenxiao Xiao, Zhengming Ding, Hongfu Liu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several cross-domain benchmark datasets demonstrate the effectiveness of our method over the state-of-the-art PDA methods. Moreover, we elaborate in-depth analyses to further explore implicit semantic alignment. |
| Researcher Affiliation | Academia | Wenxiao Xiao Department of Computer Science Brandeis University Waltham, MA 02451 wenxiaoxiao@brandeis.edu Zhengming Ding Department of Computer Science Tulane University New Orleans, LA 70118 zding1@tulane.edu Hongfu Liu Department of Computer Science Brandeis University Waltham, MA 02451 hongfuliu@brandeis.edu |
| Pseudocode | No | The paper describes the model's components and objective function verbally and with equations, but no pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | Our code will be publicly available at: https://github.com/implicitseman-align/Implicit-Semantic-Response-Alignment. |
| Open Datasets | Yes | Datasets. We include three domain adaptation benchmark datasets for performance evaluation. (1) Office-Home dataset [44] is a challenging benchmark... (2) Office31 dataset [36] contains images of 31 object categories... (3) Image Net-Caltech [4] is a large-scale object classification dataset that consists Image Net-1K (I) [35] and Caltech256 (C)... |
| Dataset Splits | Yes | For task I C, the training set of Image Net-1K is used as source domain and a subset of 84 classes from Caltech256 is used as target domain. While for task C I the Caltech256 is used as target domain and we choose the same 84 classes from the validation set of Image Net-1K as the target domain. |
| Hardware Specification | Yes | We implement our model in Py Torch [32] using one NVIDIA Titan V GPU card. |
| Software Dependencies | No | We implement our model in Py Torch [32] using one NVIDIA Titan V GPU card. The paper mentions PyTorch but does not specify its version number or any other software dependencies with specific versions. |
| Experiment Setup | Yes | We adopt the pre-trained Res Net-50 [11] network as the backbone feature extractor. ASn auto-encoder with one hidden layer... We train the network with the standard stochastic gradient descent optimizer and the learning rate is set to 1e-3 initially and decay exponentially during training. The learning rate of the backbone feature extractor is 0.1 of other layers... α and β are both set to 1 for Office-Home and Image Net-Caltech, while for Office31 we set α and β to 0.1 and 0.5. λreg is set to 0.5 in all experiments. For Office-Home, Office31 and Image Net-Caltech, the maximum iterations for training is set to 8,000, 4,000 and 40,000, respectively. The numbers of implicit semantic topics are set to 256, 64 and 16 separately for Office-Home, Office31 and Imagenet-Caltech. |