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..
On the Soft-Subnetwork for Few-Shot Class Incremental Learning
Authors: Haeyong Kang, Jaehong Yoon, Sultan Rizky Hikmawan Madjid, Sung Ju Hwang, Chang D. Yoo
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide comprehensive empirical validations demonstrating that our Soft Net effectively tackles the few-shot incremental learning problem by surpassing the performance of state-of-the-art baselines over benchmark datasets. The public code is available at https://github.com/ihaeyong/ Soft Net-FSCIL. |
| Researcher Affiliation | Academia | Haeyong Kang, Jaehong Yoon, Sultan Rizky Madjid, Sung Ju Hwang, and Chang D. Yoo Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon EMAIL |
| Pseudocode | Yes | Algorithm 1 Soft-Subnetworks (Soft Net) |
| Open Source Code | Yes | The public code is available at https://github.com/ihaeyong/ Soft Net-FSCIL. |
| Open Datasets | Yes | To validate the effectiveness of the soft-subnetwork, we follow the standard FSCIL experimental setting. We randomly select 60 classes as the base class and the remaining 40 as new classes for CIFAR-100 and mini Image Net. |
| Dataset Splits | No | During the training stage in the base session, we select top-c% weights at each layer and acquire the optimal soft-subnetworks with the best validation accuracy. However, the specific split (e.g., percentages or counts) for a dedicated validation set is not explicitly provided in the text. |
| Hardware Specification | Yes | The experiments are conducted with NVIDIA GPU RTX8000 on CUDA 11.0. |
| Software Dependencies | Yes | The experiments are conducted with NVIDIA GPU RTX8000 on CUDA 11.0. |
| Experiment Setup | Yes | In each incremental few-shot learning session, the total number of training epochs is 6, and the learning rate is 0.02. We train new class session samples using a few minor weights of the soft-subnetwork (Conv4x layer of Res Net18 and Conv3x layer of Res Net20) obtained by the base session learning. |