Open-world Semi-supervised Novel Class Discovery
Authors: Jiaming Liu, Yangqiming Wang, Tongze Zhang, Yulu Fan, Qinli Yang, Junming Shao
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three image datasets are conducted and the results show the effectiveness of the proposed method in open-world scenarios, especially with scarce known classes and labels. |
| Researcher Affiliation | Academia | University of Electronic Science and Technology of China {liujiaming, leo wang, zhangtongze, ylfan}@std.uestc.edu.cn, {qinli.yang, junmshao}@uestc.edu.cn |
| Pseudocode | No | The paper describes the proposed method in text and mathematical equations, but it does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | Yes | 1Code and appendix at https://github.com/LiuJMzzZ/OpenNCD |
| Open Datasets | Yes | Our proposed approach1 is evaluated on three widely-used datasets in the standard image classification tasks including CIFAR-10 [Krizhevsky, 2009], CIFAR-100 [Krizhevsky, 2009], and Image Net-100, which is randomly sub-sampled with 100 classes from Image Net [Deng et al., 2009] for its large volume. |
| Dataset Splits | No | The paper describes how classes are divided into known and unknown, and how labeled data is portioned (e.g., 'only 10% of the known classes are labeled'), but it does not explicitly provide the standard train/validation/test dataset splits (e.g., 80/10/10 split or specific counts) within the text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Resnet-18' and 'SimCLR' and an 'Adam optimizer', but it does not specify version numbers for any software libraries or dependencies (e.g., Python, PyTorch, TensorFlow, CUDA, scikit-learn versions). |
| Experiment Setup | Yes | 50 prototypes are utilized for the CIFAR-10 dataset and 500 for both the CIFAR-100 and Image Net-100 datasets with a fixed dimension of 32. We adopt an Adam optimizer with a learning rate of 0.002 and fix the batch size to 512 in all experiments. The temperature scale τ is set to 0.1 suggested by most of the previous methods, and the weight of the last two terms in the objective function is set to {λ1, λ2} = {1, 1}. κ is set to 5 in prototype grouping. |