Towards Robust Model Reuse in the Presence of Latent Domains
Authors: Jie-Jing Shao, Zhanzhan Cheng, Yu-Feng Li, Shiliang Pu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on diverse real-world data sets clearly validate the effectiveness of proposed algorithms. and 4 Empirical Study To validate our method, we perform experiments on diverse tasks, including Digital Recognition, Attribute Classification and Face Recognition. |
| Researcher Affiliation | Collaboration | Jie-Jing Shao1 , Zhanzhan Cheng2 , Yu-Feng Li1 and Shiliang Pu2 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2Hikvision Research Institute, Hangzhou, China {shaojj, liyf}@lamda.nju.edu.cn, {chengzhanzhan, pushiliang.hri}@hikvision.com, |
| Pseudocode | Yes | Algorithm 1 The proposed MRL method |
| Open Source Code | No | The paper includes '1https://pytorch.org/' as a footnote, which refers to a third-party library used, not the authors' own source code for the proposed method. No explicit statement about releasing their code or a link to a repository for their method was found. |
| Open Datasets | Yes | Digital Recognition MNIST SVHN USPS... Our second set of experiments is based on the Animals with Attributes 2 dataset2, which contains 37,322 images of 50 animal classes. 2https://cvml.ist.ac.at/Aw A2/... Finally, we evaluate our method on the CMU Multi-PIE dataset [Sim et al., 2002]... These subsets3 are based on SURF features and the dimension of features is 1024. 3https://github.com/jindongwang/transferlearning/blob/master/ data/dataset.md |
| Dataset Splits | Yes | During reuse, we take 10% samples for validation and 40% samples for testing. and For the remaining 10,000 samples, we use 50% as training set, 10% examples as validation set and take the others for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments were provided. The paper only mentions that methods are implemented on PyTorch. |
| Software Dependencies | No | The paper mentions 'Py Torch1' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | No | The paper mentions 'The hyper-parameters are adjusted by the validation set for all methods.' but does not provide specific hyperparameter values (e.g., learning rate, batch size, epochs, optimizer settings) or detailed training configurations. |