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 Bias Correction for Task-Free Continual Learning
Authors: Aristotelis Chrysakis, Marie-Francine Moens
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 20,627 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,644 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 evaluate the performance of OBC extensively, and we show that it significantly improves a number of task-free continual learning methods, over multiple datasets (Section 4). |
| Researcher Affiliation | Academia | Aristotelis Chrysakis & Marie-Francine Moens Department of Computer Science KU Leuven Leuven, Belgium |
| Pseudocode | Yes | Algorithm 1 Online Bias Correction |
| Open Source Code | Yes | Our code can be found at https://github.com/chrysakis/OBC. |
| Open Datasets | Yes | The Fashion MNIST dataset (Xiao et al., 2017) contains 60,000 grayscale images of clothing items split in 10 classes. CIFAR-10 and CIFAR-100 (Krizhevsky, 2009) each contain 50,000 color images... Finally, tiny Image Net (Le & Yang, 2015) contains 100,000 color images... |
| Dataset Splits | Yes | As Lomonaco & Maltoni (2017) suggest, sessions 3, 7, and 10 are used for evaluation purposes (approximately 45,000 images), and the remaining 8 sessions are used to construct the stream (approximately 120,000 images). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using specific architectures like Res Net-18 and CNN, implying the use of machine learning frameworks (e.g., PyTorch), but it does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | we use a learning rate of 0.1 when using the reduced Res Net-18 architecture. When using the simpler CNN, we use a learning rate of 0.03. The stream and replay batch sizes were both set to 10... The batch size of OBC was set to 50... and a label-smoothing factor of 0.5 |