DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning
Authors: Huiping Zhuang, Run He, Kai Tong, Ziqian Zeng, Cen Chen, Zhiping Lin
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that the DS-AL, despite being an exemplar-free technique, delivers performance comparable with or better than that of replay-based methods across various datasets, including CIFAR-100, Image Net100 and Image Net-Full. |
| Researcher Affiliation | Academia | Huiping Zhuang1, Run He1, Kai Tong1, Ziqian Zeng1, Cen Chen2,3*, Zhiping Lin4 1Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, China 2 School of Future Technology, South China University of Technology, China 3 Pazhou Laboratory, China 4 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore {hpzhuang, zqzeng, chencen}@scut.edu.cn, {wirh, wikaitong}@mail.scut.edu.cn, ezplin@ntu.edu.sg |
| Pseudocode | Yes | The DS-AL is summarized in algorithm framework (we place the algorithm in Supplementary material B. |
| Open Source Code | Yes | Our codes are available at https: //github.com/ZHUANGHP/Analytic-continual-learning. |
| Open Datasets | Yes | Datasets include CIFAR-100, Image Net100 and Image Net-Full. |
| Dataset Splits | No | The paper describes training data for each phase (Dtrain k) and testing datasets (Dtest k) but does not explicitly detail a separate validation dataset split. |
| Hardware Specification | No | Not found. The paper does not specify the hardware used for the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | Not found in the main text. The paper refers to training strategies from ACIL in supplementary material but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Hyperparameters. Two unique hyperparameters (i.e., σC and C) have been introduced in this paper. We utilize grid search to determine their values. ... The activation function chosen in DS-AL is Tanh. The compensation ratios C = 0.6, 0.8, 1.4 on CIFAR-100, Image Net-100 and Image Net-Full respectively. |