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..
Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective
Authors: Joonkyu Kim, Yejin Kim, Jy-Yong Sohn
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Third, we support our theoretical findings through experiments on real image datasets, including Split-CIFAR100 and Image Net1K. |
| Researcher Affiliation | Academia | 1Department of Electrical & Electronic Engineering, Yonsei University, Seoul, South Korea 2Department of Statistics and Data Science, Yonsei University, Seoul, South Korea. |
| Pseudocode | No | The paper includes mathematical derivations and proofs but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements about the availability of open-source code, nor does it provide any links to code repositories. |
| Open Datasets | Yes | We test on Split-CIFAR100 dataset (Ramasesh et al., 2020) and a downsampled version of the original Image Net1K dataset (Chrabaszcz et al., 2017). |
| Dataset Splits | No | The paper mentions that for each dataset, the classes are partitioned into N = 50 categories, with each category containing 2 classes for Split-CIFAR100 and 5 classes for Image Net1K. It also mentions training a linear classifier for task t=1 on extracted features. However, it does not provide specific training/validation/test split percentages or absolute sample counts for the datasets themselves. |
| Hardware Specification | No | The paper describes the experimental setup, including datasets and model architecture, but does not specify any particular hardware components such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions "OPTIMIZER ADAMW" in Table 1, but does not provide specific version numbers for this or any other key software libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | Table 1. Continual learning hyper-parameter for Image Net32 training PARAMETER VALUE LEARNING RATE 0.001 BATCH SIZE 512 EPOCHS 500 WARM UP STEPS 200 WORKERS 4 OPTIMIZER ADAMW WEIGHT DECAY 5E-4 |