SemiReward: A General Reward Model for Semi-supervised Learning
Authors: Siyuan Li, Weiyang Jin, Zedong Wang, Fang Wu, Zicheng Liu, Cheng Tan, Stan Z. Li
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive experiments verify that Semi Reward achieves significant performance gains and faster convergence speeds upon Pseudo Label, Flex Match, and Free/Soft Match. |
| Researcher Affiliation | Collaboration | Siyuan Li1,2 Weiyang Jin2 Zedong Wang2 Fang Wu2 Zicheng Liu1,2 Cheng Tan1,2 Stan Z. Li2 AI Lab, Research Center for Industries of the Future, Hangzhou, China; 1Zhejiang University, College of Computer Science and Technology; 2Westlake University |
| Pseudocode | Yes | Algorithm 1 Pseudocode of Semi Reward training and inference in a Py Torch-like style. |
| Open Source Code | Yes | Code and models are available at https://github.com/Westlake-AI/SemiReward. |
| Open Datasets | Yes | For CV tasks, our investigations featured the deployment of renowned and challenging datasets, including CIFAR-100 (Krizhevsky et al., 2009), STL-10 (Coates et al., 2011), Euro SAT (Helber et al., 2019), and Image Net (Deng et al., 2009)... (further details in Tables A1 and A2 with citations) |
| Dataset Splits | Yes | Table A1: Settings and details classification datasets in various modalities. Domain Dataset #Label per class #Training data #Validation data #Test data #Class |
| Hardware Specification | Yes | All experiments are implemented with Py Torch and run on NVIDIA A100 GPUs, using 4GPUs training by default. |
| Software Dependencies | No | All experiments are implemented with Py Torch and run on NVIDIA A100 GPUs, using 4GPUs training by default. No specific version number for PyTorch or other libraries is provided. |
| Experiment Setup | Yes | Table A3: Hyper-parameters and training schemes of SSL classification tasks based on USB. and Table A4: Hyper-parameters and training schemes of Semi Reward for various tasks and modalities. These tables specify learning rates, batch sizes, optimizers, schedulers, etc. |