Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Delve into Base-Novel Confusion: Redundancy Exploration for Few-Shot Class-Incremental Learning
Authors: Haichen Zhou, Yixiong Zou, Ruixuan Li, Yuhua Li, Kui Xiao
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across benchmark datasets, including CIFAR-100, mini Image Net, and CUB-2002011 demonstrate that our method achieves stateof-the-art performance. |
| Researcher Affiliation | Academia | Haichen Zhou1 , Yixiong Zou1 , Ruixuan Li1 , Yuhua Li1 and Kui Xiao2 1Huazhong University of Science and Technology 2Hubei University |
| Pseudocode | No | The paper describes its method in prose and mathematical equations but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | Our method is evaluated on CIFAR-100 [Krizhevsky et al., 2009], mini Image Net [Russakovsky et al., 2015], and CUB200 [Wah et al., 2011]. |
| Dataset Splits | No | The paper describes the incremental learning setup (e.g., 60 base classes and 40 novel classes across 8 incremental sessions), but it does not specify explicit train/validation/test dataset splits in terms of percentages or fixed sample counts that are commonly provided for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper mentions utilizing ResNet-12 and pretrained ResNet-18 models but does not specify the versions of any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | Our training protocol follows [Yang et al., 2023], utilizing the Res Net-12 for CIFAR-100 and mini Image Net, and pretrained Res Net-18 for CUB200. Batch sizes of 128 and 100 are employed for the base session and incremental sessions, respectively. ... λ and β are hyper-parameres. |