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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network
Authors: Jong-Yeong Kim, Dong-Wan Choi8137-8145
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,207 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,181 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 | In our thorough experimental analysis, our Split-and-Bridge method outperforms the state-of-the-art competitors in KD-based continual learning. |
| Researcher Affiliation | Academia | Jong-Yeong Kim, Dong-Wan Choi Department of Computer Science and Engineering, Inha University, South Korea EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Split-and-Bridge Incremental Learning |
| Open Source Code | Yes | 2https://github.com/bigdata-inha/Split-and-Bridge |
| Open Datasets | Yes | In our experiments, we train two benchmark datasets, CIFAR-100 (Krizhevsky, Hinton et al. 2009) and Tiny-Image Net (Le and Yang 2015) |
| Dataset Splits | Yes | Tiny-Image Net includes 100K training images and 10K validation images for 200 classes |
| Hardware Specification | Yes | We implement all the methods2 in Py Torch, and train each model on a machine with an NVIDIA TITAN RTX and Intel Core Xeon Gold 5122. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | In the split phase, we divide two last residual blocks along with the ๏ฌnal fully-connected (FC) layer of Res Net-18 into two disjoint partitions, i.e., ฮธo and ฮธn, implying S = 13 and L = 18. Full details are covered in our supplementary material. |