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..
Few-Shot Lifelong Learning
Authors: Pratik Mazumder, Pravendra Singh, Piyush Rai2337-2345
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that our method significantly outperforms existing methods on the mini Image Net, CIFAR-100, and CUB-200 datasets. Specifically, we outperform the state-of-the-art method by an absolute margin of 19.27% for the CUB dataset. |
| Researcher Affiliation | Collaboration | Pratik Mazumder*1, Pravendra Singh 2, Piyush Rai1 1 Department of Computer Science and Engineering, IIT Kanpur, India 2 Independent Researcher, India |
| Pseudocode | No | The paper describes the proposed method in text and with mathematical equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We perform experiments in the FSCIL setting using three image classification datasets CIFAR-100 (Krizhevsky and Hinton 2009), mini Image Net (Vinyals et al. 2016) and CUB200 (Wah et al. 2011). |
| Dataset Splits | No | The paper describes the construction of training and test sets but does not explicitly mention a distinct validation set or its split details for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper mentions using a "Res Net-18 architecture" for experiments, which is a model architecture, but does not provide any specific details about the hardware used (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper mentions implementing the method but does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We train Θ(1) F , and Θ(1) C on the base training set D(1) with an initial learning rate of 0.1 and mini-batch size of 128. After the 30 and 40 epochs, we reduce the learning rate to 0.01 and 0.001, respectively. We train on D(1) for a total of 50 epochs and then discard Θ(1) C . We finetune the feature extractor on each of the few-shot training sets D(t>1) for 30 epochs, with a learning rate of 1e-4 (and 1e-3 for CUB-200). |