Compositional Prototypical Networks for Few-Shot Classification
Authors: Qiang Lyu, Weiqiang Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method can achieve state-of-the-art results on different datasets and settings. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, University of Chinese Academy of Sciences |
| Pseudocode | No | The paper describes its method using textual explanations and equations, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/fikry102/CPN. |
| Open Datasets | Yes | We conduct the experiments on two datasets with human-annotated attribute annotations: Caltech UCSD-Birds 200-2011 (CUB) (Wah et al. 2011), and SUN Attribute Database (SUN) (Patterson et al. 2014). |
| Dataset Splits | Yes | As (Chen et al. 2019a), we divide CUB into 100 training classes, 50 validation classes, and 50 testing classes. As (Huang et al. 2021; Ji et al. 2022), we divide SUN into 580 training classes, 65 validation classes, and 72 testing classes. |
| Hardware Specification | No | The paper mentions using 'Res Net12' and 'Conv4' as feature extractors, which are neural network architectures, not specific hardware. No details about GPUs, CPUs, or other hardware used for computation are provided. |
| Software Dependencies | No | The paper mentions software components like 'Res Net12', 'Conv4', 'SGD optimizer', and 'Dropblock regularization', but it does not specify any version numbers for these software dependencies (e.g., PyTorch or TensorFlow versions). |
| Experiment Setup | Yes | We use the SGD optimizer with a momentum of 0.9 and weight decay of 5 10 4. Following (Yang, Wang, and Chen 2022), we adopt the random crop, random horizontal flip and erasing, and color jittering to perform data augmentation. Dropblock (Ghiasi, Lin, and Le 2018) regularization is used to reduce overfitting. In the pre-training stage, we train the feature extractor for 30 epochs. In the meta-training stage, we train our model for 10 epochs. |