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 | Conference PDF | Archive PDF | Plain Text | 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 {haeyong.kang,jaehong.yoon,suulkyy,sjhwang82,cd yoo}@kaist.ac.kr |
| 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. |