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..
Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting
Authors: Tiange Zhang, Qing Cai, Feng Gao, Lin Qi, Junyu Dong
IJCAI 2024 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |