Continual Learning with Filter Atom Swapping
Authors: Zichen Miao, Ze Wang, Wei Chen, Qiang Qiu
ICLR 2022 | Venue PDF | Archive PDF | Plain Text | 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. |