Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation
Authors: Yixiong Zou, Shanghang Zhang, Yuhua Li, Ruixuan Li
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on CIFAR100, Caltech-USCD Birds200-2011 (CUB200), and mini Image Net demonstrate that the proposed method effectively mitigates the CO problem and achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Yixiong Zou1, Shanghang Zhang2, Yuhua Li1 and Ruixuan Li1 1School of Computer Science and Technology, Huazhong University of Science and Technology 2School of Computer Science, Peking University 1{yixiongz, idcliyuhua, rxli}@hust.edu.cn, 2shanghang@pku.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation is based on CEC s code [28], and our code will be released3. 3https://github.com/Zoilsen/CLOM |
| Open Datasets | Yes | Datasets include CIFAR100 [15], Caltech-UCSD Birds-200-2011 (CUB200) [24] and mini Image Net [23] as listed in Tab. 3 following the split in [22]. |
| Dataset Splits | Yes | CIFAR100 contains 100 classes in all. As split by [22], 60 classes are chosen as base classes, and the remaining 40 classes (with 5 training samples in each class) are chosen as novel classes2. Table 3: Evaluation datasets. Dataset Total Classes Base Classes Novel Classes Incremental Sessions Novel-Class Shot Input Size CIFAR100 100 60 40 8 5 32 32 CUB200 200 100 100 10 5 224 224 mini Image Net 100 60 40 8 5 84 84 |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions that 'The implementation is based on CEC s code [28]', but does not provide specific version numbers for software dependencies such as libraries or frameworks. |
| Experiment Setup | Yes | For CIFAR100, we set d P =256, set mave=-0.2, set mupper=-0.5, and we have m P ave=0.1 and m P upper=0.2. For CUB200, we scale the learning rate of the backbone network to 10% of the global learning rate since the pretraining of the backbone is adopted [30, 28], and set d P to 8192. Then we have mave=-0.2 and mupper=-0.25 and m P ave=0.3 and m P upper=0.6. For mini Image Net, we set d P to 4096, and have mave=-0.2 and mupper=-0.5 and m P ave=0.1 and m P upper=0.2. |