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..
Continual Learning with Filter Atom Swapping
Authors: Zichen Miao, Ze Wang, Wei Chen, Qiang Qiu
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Being validated on multiple benchmark datasets with different convolutional network structures, the proposed method outperforms the state-of-the-art methods in both accuracy and scalability. |
| Researcher Affiliation | Academia | Zichen Miao, Ze Wang, Wei Chen, Qiang Qiu Department of ECE Purdue University EMAIL |
| Pseudocode | Yes | We provide the algorithm of the proposed method in Alg. 1. |
| Open Source Code | No | The paper does not provide explicit statements or links for open-source code availability. |
| Open Datasets | Yes | We validate our method with the Class-Incremental (CI) setting with CIFAR100 and Image Net-Subset , which contains 100 classes selected from Image Net (with random seed 1993). |
| Dataset Splits | Yes | Details of each dataset are provided in the Appendix C. Table A: Statistics of 10-Split CIFAR100. # Training samples/task 4500 # Validation samples/task 500 # Test samples/task 100 |
| Hardware Specification | Yes | All methods are tested on a single RTX 2080ti GPU under the class-incremental setting. |
| Software Dependencies | No | The paper mentions software like SGD, ResNet, and AlexNet-like architectures but does not specify their version numbers. |
| Experiment Setup | Yes | For CIFAR100, we choose SGD with batch-size of 128, learning rate of 0.01, momentum of 0.9 and weight decay 1e-3. The model is trained for 250 epochs, with learning rate drop by 0.1 at the 100-th and 200-th epoch. |