Fast Generalized Distillation for Semi-Supervised Domain Adaptation
Authors: Shuang Ao, Xiang Li, Charles Ling
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that GDSDA-SVM can effectively utilize the unlabeled data to transfer the knowledge between different domains under the SDA setting. |
| Researcher Affiliation | Academia | Shuang Ao, Xiang Li, Charles X. Ling Department of Computer Science, The University of Western Ontario sao@uwo.ca, lxiang2@uwo.ca, cling@csd.uwo.ca |
| Pseudocode | Yes | Algorithm 1 GDSDA-SVM, Algorithm 2 λ Optimization |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Dataset: We use the domain adaptation benchmark dataset Office as our experiment dataset. There are 3 subsets in Office dataset, Webcam (795 examples), Amazon (2817 examples) and DSLR (498 examples), sharing 31 classes. We denote them as W, A and D respectively. |
| Dataset Splits | Yes | By minimizing the LOOCV loss on the target data, we can find the optimal imitation parameter. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Alexnet', 'multi-layer perception (MLP)', 'SVM', and 'LIBLINEAR', but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For GDSDA-SVM, as we are not able to tune the temperature T, we empirically set T = 20 for all experiments in this subsection. Specifically, we search the imitation parameter λ1 in the range [0, 0.1, ..., 1] with different temperature T. We use temperature T = 5. |