Masked Feature Generation Network for Few-Shot Learning
Authors: Yunlong Yu, Dingyi Zhang, Zhong Ji
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In extensive experiments on four FSL benchmarks, MFGN performs competitively and outperforms the state-of-the-art competitors on most of the few-shot classification tasks. |
| Researcher Affiliation | Academia | Yunlong Yu1 , Dingyi Zhang1 , Zhong Ji2 1 College of Information Science and Electronic Engineering, Zhejiang University 2 School of Electrical and Information Engineering, Tianjin University {yuyunlong, dyzhang}@zju.edu.cn, jizhong@tju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Pseudocode of MFGN in a Py Torch-like style. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We have conducted experiments on four datasets that are Caltech UCSD Birds (CUB) [Wah et al., 2011], Stanford Dogs [Khosla et al., 2011], mini Image Net [Vinyals et al., 2016], and CIFAR-FS [Bertinetto et al., 2018]. |
| Dataset Splits | Yes | Following the split proposed in [Chen et al., 2018], we divide the data into three disjoint sets, 100, 50, and 50 categories, respectively for training, validation, and test. ... Following the split proposed in [Ravi and Larochelle, 2016], we divide the dataset with 64, 16, and 20 classes for training, validation, and test, respectively. The CIFAR-FS is a few-shot classification dataset built on the CIFAR100. Following [Bertinetto et al., 2018], we use 64/16/20 classes for training/validation/test, respectively. |
| Hardware Specification | No | The paper mentions using a CNN backbone and a transformer, but does not provide any specific details about the hardware (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch-like style' pseudocode but does not list specific software dependencies with version numbers, such as PyTorch version, Python version, or other libraries. |
| Experiment Setup | Yes | To train the model, we apply the SGD optimizer with a momentum of 0.9 and a weight decay of 5e 4. The batch size is set to 32. For the fine-grained datasets, we train the models with 80 epochs. The learning rate is initialized as 0.05 and decayed with a factor of 0.1 at 60 and 70 epochs, respectively. For the coarse-grained datasets, we train the models with 60 epochs. The learning rate is initialized as 0.05 and decayed with a factor of 0.1 at 40 and 50 epochs, respectively. ... If not specific, both λ and γ are set to 1; the number of synthesized visual features is set to 5 for all datasets. |