Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Coreset Selection for Rehearsal-based Continual Learning
Authors: Jaehong Yoon, Divyam Madaan, Eunho Yang, Sung Ju Hwang
ICLR 2022 | Venue PDF | LLM Run Details | Input Tokens: 26,349 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,576 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of our coreset selection mechanism over various standard, imbalanced, and noisy datasets against strong continual learning baselines, demonstrating that it improves task adaptation and prevents catastrophic forgetting in a sample-efficient manner. 5 EXPERIMENTS |
| Researcher Affiliation | Collaboration | Jaehong Yoon1 Divyam Madaan2 Eunho Yang1,3 Sung Ju Hwang1,3 KAIST1 New York University2 AITRICS3 |
| Pseudocode | Yes | Algorithm 1 Online Coreset Selection (OCS) |
| Open Source Code | No | The paper does not explicitly state that the source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Datasets. We validate OCS on domain-incremental CL for Balanced and Imbalanced Rotated MNIST using a single-head two-layer MLP... We use the Long-Tailed CIFAR-100 (Cui et al., 2019)... This dataset contains a sequence of five benchmark datasets: MNIST (Le Cun et al., 1998), fashion-MNIST (Xiao et al., 2017), Not MNIST (Bulatov, 2011), Traffic Sign (Stallkamp et al., 2011), and SVHN (Netzer et al., 2011)... |
| Dataset Splits | No | The paper describes how data is used within tasks (e.g., 'standard single epoch training'), and mentions 'test accuracy', but does not explicitly provide percentages or counts for distinct training, validation, and test dataset splits in the general sense for all experiments, nor does it explicitly mention a separate validation set for hyperparameter tuning. |
| Hardware Specification | Yes | The running time reported in Table 5 was measured on a single NVIDIA TITAN Xp. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Table A.7 shows the initial learning rate, learning rate decay, and batch size for each dataset that are shared among all the methods. |