Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting
Authors: Tiange Zhang, Qing Cai, Feng Gao, Lin Qi, Junyu Dong
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on CDFSL benchmarks demonstrate the effectiveness of our proposed method as well as robustly improve the performance of existing CD-FLS methods. |
| Researcher Affiliation | Academia | Faculty of Computer Science and Technology, Ocean University of China zhangtiange@stu.ouc.edu.cn, {cq, gaofeng, qilin, dongjunyu}@ouc.edu.cn |
| Pseudocode | No | Other details can be viewed at the Algorithm. 1 in supplements. |
| Open Source Code | Yes | Resources at https://github.com/tinkez/FAP CDFSC. |
| Open Datasets | Yes | We conduct extensive experiments under the very strict cross-domain few-shot learning settings where only one single source dataset is used for meta-training, i.e., mini Imagne Net [Vinyals et al., 2016]. There are two CD-FSL benchmarks used in the meta-testing phase for evaluation. One is from the FWT benchmark [Tseng et al., 2020] which includes CUB [Welinder et al., 2010], Cars [Krause et al., 2013], Places [Zhou et al., 2017], and Plantae [Van Horn et al., 2018]. The other one is the BSCD-FSL benchmark [Guo et al., 2020] which consists of Chest X, ISIC, Euro SAT, and Crop Disease. |
| Dataset Splits | Yes | For all experiments, we select the model with the best validation accuracy on mini-Image Net for the next testing on eight target datasets. |
| Hardware Specification | Yes | All experiments reported are conducted on the RTX 2080Ti GPUs. |
| Software Dependencies | No | The paper mentions using 'Adam' as an optimizer and 'Res Net-10' as a feature extractor, but it does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | For a fair comparison, Res Net-10 is selected as the feature extractor and the optimizer is Adam with a fixed learning rate α = 0.001. The iteration number for early stopping is kept as Tmax = 5 in all experiments and the gradient ascending step takes a learning rate β from {20, 40, 60, 80}. We maintain the Random Convolution layer applied in [Wang and Deng, 2021] with a probability (1 p), where p is selected from {0.5, 0.6, 0.7}. The filter size k is randomly sampled from the candidate pool K = {1, 3, 5, 7, 11, 15} and the Xavier normal distribution [Glorot and Bengio, 2010] is used to initialize the layer weights. Both stride and padding sizes are determined to keep the image size unchanged. |