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..
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
Authors: Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, De-Chuan Zhan
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets validate MEMO s competitive performance. |
| Researcher Affiliation | Academia | State Key Laboratory for Novel Software Technology, Nanjing University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/wangkiw/ICLR23-MEMO |
| Open Datasets | Yes | we evaluate the performance on CIFAR100 (Krizhevsky et al., 2009), and Image Net100/1000 (Deng et al., 2009). |
| Dataset Splits | Yes | CIFAR100 contains 50,000 training and 10,000 testing images, with a total of 100 classes. Image Net is a large-scale dataset with 1,000 classes, with about 1.28 million images for training and 50,000 for validation. The class order of training classes is shuffled with random seed 1993. |
| Hardware Specification | Yes | All models are deployed with Py Torch (Paszke et al., 2019) and Py CIL (Zhou et al., 2021a) on NVIDIA 3090. |
| Software Dependencies | No | The paper mentions PyTorch and PyCIL with citations but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The model is trained with a batch size of 128 for 170 epochs, and we use SGD with momentum for optimization. The learning rate starts from 0.1 and decays by 0.1 at 80 and 150 epochs. |