Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
Authors: Wenyu Zhang, Li Shen, Wanyue Zhang, Chuan-Sheng Foo
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 5 cross-domain classification and 4 semantic segmentation datasets show that our method achieves more accurate and reliable performance than test-time adaptation, while not being constrained by streaming conditions. |
| Researcher Affiliation | Academia | Wenyu Zhang1 , Li Shen1 , Wanyue Zhang2 and Chuan-Sheng Foo1,3 1Institute for Infocomm Research, A*STAR 2Max Planck Institute for Informatics 3Centre for Frontier AI Research, A*STAR |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We evaluate on image classification and segmentation tasks using publicly available benchmark datasets and compare our proposed method to existing source-free methods. For each dataset, our source models are the base models trained on source domain(s) using empirical risk minimization (ERM) or DG methods that have state-of-the-art performance on that dataset. |
| Dataset Splits | No | The paper mentions training, but does not explicitly provide details about specific training/validation/test splits, such as percentages, sample counts, or a cross-validation setup, in the main text. |
| Hardware Specification | Yes | Empirically, on Vis DA using a Tesla V100-SXM2 GPU, the average training time per epoch on a vanilla Res Net-101 is 3.18s, 3.56s and 4.32s for k = 1, 5 and 10 respectively. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We set m = 10 epochs in all our experiments. Support samples are augmented with the same data augmentations for source model training. We use a mini-batch size of 32 for classification and 1 for segmentation, and use the Adam optimizer with 0.001 learning rate for finetuning LCCS. |