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 Bag of Tricks for Few-Shot Class-Incremental Learning
Authors: Shuvendu Roy, Chunjong Park, Aldi Fahrezi, Ali Etemad
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments on three benchmark datasets, CIFAR-100, CUB-200, and mini IMage Net, to evaluate the impact of our proposed framework. Our detailed analysis shows that our approach substantially improves both stability and adaptability, establishing a new state-of-the-art by outperforming prior works in the area. |
| Researcher Affiliation | Collaboration | Shuvendu Roy1,2 , Chunjong Park1, Aldi Fahrezi1, Ali Etemad1,2 1Google Research 2Queen s University, Canada EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper includes mathematical equations for loss functions and masks but does not present any structured pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide a direct link to a source code repository, nor does it explicitly state that the code for the described methodology will be made publicly available or is included in supplementary materials. |
| Open Datasets | Yes | Following the established protocol in the FSCIL literature, we conduct our experiments on three popular datasets: CIFAR100 (Krizhevsky et al., 2009), mini Image Net (Russakovsky et al., 2015) and CUB200 (Wah et al., 2011). |
| Dataset Splits | Yes | Specifically, for CIFAR-100 and mini Image Net, we use 60 classes for the base session and 40 classes for the incremental sessions. The incremental learning experiments are conducted on a 5-way, 5-shot setting. In the case of CUB-200, we allocate 100 classes for the base session and another 100 classes for the incremental sessions, each containing ten classes (10-way, 5-shot). |
| Hardware Specification | Yes | In Table 6, we discuss the time complexity of our framework using a single Nvidia RTX 2080 GPU in comparison to SAVC (Song et al., 2023). |
| Software Dependencies | No | The paper mentions using a Res Net-18 encoder and an SGD optimizer but does not specify software versions for programming languages, libraries, or frameworks like Python, PyTorch, TensorFlow, or CUDA. |
| Experiment Setup | Yes | We train the model with an SGD optimizer, a momentum of 0.9, and a batch size of 64. The learning rate is set to 0.1 for CIFAR-100 and mini Image Net and 0.001 for CUB-200. For all experiments, the model is trained on an Nvidia RTX 2080 GPU. Our findings from this study show that the best results for CIFAR-100, CUB-200 and mini Image Net datasets are obtained for training 400, 80, and 80 epochs, respectively. |