Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork
Authors: Qiang Gao, Xiaojun Shan, Yuchen Zhang, Fan Zhou
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 19,873 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,374 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 | The comprehensive experiments conducted on four benchmark datasets demonstrate the effectiveness of the proposed DSN in the context of task-incremental learning by comparing it to several state-of-the-art baselines. |
| Researcher Affiliation | Academia | 1Southwestern University of Finance and Economics, Chengdu, China 2University of Electronic Science and Technology of China, Chengdu, China |
| Pseudocode | Yes | Algorithm 1: Incremental learning with DSN. |
| Open Source Code | Yes | The source codes are available at https://github.com/shanxiaojun/DSN. |
| Open Datasets | Yes | We employ four benchmark datasets for TIL problem as follows: Permuted MNIST (PMNIST) [3], Rotated MNIST (RMNIST) [21], Incremental CIFAR-100 [54, 55], and Tiny Image Net [19]. PMNIST encompasses 10 variations of MNIST [56]... The original CIFAR-100 was divided into 20 tasks... Tiny Image Net constitutes a variant of Image Net [57]... |
| Dataset Splits | No | The paper mentions 'test sets' but does not explicitly provide details about train/validation/test dataset splits, such as percentages, sample counts, or a specific splitting methodology in the main text. |
| Hardware Specification | No | The paper does not specify any particular hardware components like CPU or GPU models used for experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | For two variants of MNIST, we adopt the experimental setup outlined in [44] in which we use a two-layer MLP, a multi-head classifier, and begin with 2000-2000-10 neurons for the first task. ...We evaluate the impact of η that aims to hold more room for future tasks. ... η = 0 indicates that we do not penalize the number of activated neurons for each task. |