Symmetric Self-Paced Learning for Domain Generalization
Authors: Di Zhao, Yun Sing Koh, Gillian Dobbie, Hongsheng Hu, Philippe Fournier-Viger
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments across five popular benchmark datasets demonstrate the effectiveness of the proposed learning strategy. Experiments conducted on five benchmark datasets, including Digits, PACS, Office-Home, VLCS, and NICO++, demonstrate the effectiveness of the proposed learning strategy. Ablation studies further validate the effectiveness and robustness of the proposed training scheduler and difficulty measure in domain generalization. |
| Researcher Affiliation | Academia | Di Zhao 1, Yun Sing Koh1, Gillian Dobbie1, Hongsheng Hu2, Philippe Fournier-Viger3 1 School of Computer Science, University of Auckland 2 CSIRO s Data61 3 College of Computer Science and Software Engineering Shenzhen University |
| Pseudocode | Yes | The structure of SSPL is illustrated in Figure 2, and the algorithm is summarized in Algorithm 1. |
| Open Source Code | Yes | The code is available in https://github.com/RobustMM/VIGIL. |
| Open Datasets | Yes | The proposed approach is evaluated on five popular domain generalization benchmark datasets, which cover a variety of image classification problems. (1) Digits (Zhou et al. 2020) consists of four digit recognition tasks, namely MNIST (Le Cun et al. 1998), MNIST-M (Ganin and Lempitsky 2015), SVHN (Netzer et al. 2011), and SYN (Ganin and Lempitsky 2015). (2) PACS (Li et al. 2017b) consists of four domains... (3) Office-Home (Venkateswara et al. 2017)... (4) VLCS (Fang, Xu, and Rockmore 2013)... (5) NICO++ (Zhang et al. 2023)... |
| Dataset Splits | No | The paper describes a 'leave-one-out-test evaluation strategy' where remaining domains are used as source domains for training and one for testing. It does not explicitly mention a separate validation split used for hyperparameter tuning or model selection during training, distinct from the test set. |
| Hardware Specification | Yes | All experiments are conducted on NVIDIA Tesla A100 GPUs. |
| Software Dependencies | No | The paper mentions 'Our methodology is implemented using the Py Torch libraries.' but does not specify the version number of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The optimizer utilized for training is Stochastic Gradient Descent (SGD), with a momentum of 0.9 and a weight decay of 5e-4. For the Digits dataset, we train the networks with an initial learning rate of 0.05 and a batch size of 64 for 50 epochs. The learning rate is decayed by a factor of 0.1 every 20 epochs. For the PACS, Office Home, and VLCS datasets, the networks are trained with a learning rate of 0.01 and a batch size of 32 for 50 epochs. For the NICO++ dataset, the networks are trained with a learning rate of 0.005 and a batch size of 64 for 50 epochs. |