Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding
Authors: Haoliang Li, Sinno Jialin Pan, Renjie Wan, Alex C. Kot8602-8609
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on two different vision tasks to demonstrate the effectiveness of our proposed method compared with a number of baseline methods. |
| Researcher Affiliation | Academia | 1Rapid-Rich Object Search (ROSE) Lab, Nanyang Technological University, Singapore 2School of Computer Science and Engineering, Nanyang Technological University, Singapore |
| Pseudocode | Yes | Algorithm 1 Deep-MCA |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We follow the setting (Tsai, Yeh, and Frank Wang 2016; Yan et al. 2018) by using images collected from Amazon dataset (A), DSLR dataset (D), webcam dataset (W) and Caltech-256 dataset (C), where ten common categories in all these datasets are used for conduct experiments. We apply NUS-WIDE (Chua et al. 2009) and Image Net (Deng et al. 2009) as the datasets for text-to-image classification task. |
| Dataset Splits | No | The paper mentions training data selection and remaining instances for testing, but does not explicitly specify a validation set or a clear train/validation/test split percentages/counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Caffe' and optimization algorithms like 'ADAM' and 'GAN' but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The learning rate of our algorithm is set as 0.0001 for all experiments. Regarding the parameter setting for objective, one can use a tuning strategy by training on source domain and testing on labeled target domain. In our experiment, we fix the parameters for all experiments for simplicity. In particular, we set λ = 0.001, ζ = 10 and all others as 1. We set the dimension of hidden layer as 100 for fair comparison with other baseline methods. |