RankDNN: Learning to Rank for Few-Shot Learning
Authors: Qianyu Guo, Gong Haotong, Xujun Wei, Yanwei Fu, Yizhou Yu, Wenqiang Zhang, Weifeng Ge
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that Rank DNN can effectively improve the performance of its baselines based on a variety of backbones and it outperforms previous state-of-the-art algorithms on multiple few-shot learning benchmarks, including mini Image Net, tiered Image Net, Caltech-UCSD Birds, and CIFAR-FS. Furthermore, experiments on the cross-domain challenge demonstrate the superior transferability of Rank DNN. |
| Researcher Affiliation | Academia | Qianyu Guo1,2, Gong Haotong1, Xujun Wei1,3, Yanwei Fu2, Yizhou Yu4, Wenqiang Zhang2.3, Weifeng Ge1,2* 1Nebula AI Group, School of Computer Science, Fudan University,Shanghai,China 2Shanghai Key Laboratory of Intelligent Information Processing,Shanghai,China 3Academy for Engineering & Technology, Fudan University,Shanghai,China 4Department of Computer Science, The University of Hong Kong,Hong Kong,China wfge@fudan.edu.cn |
| Pseudocode | Yes | Algorithm 1: Meta-Training of Rank DNN for Few-shot Learning |
| Open Source Code | Yes | The code is available at: https://github.com/guoqianyu-alberta/Rank DNN. |
| Open Datasets | Yes | We use four popular benchmark datasets in our experiments: mini Image Net(Vinyals et al. 2016), tiered Image Net (Ren et al. 2018), Caltech-UCSD Birds-200-2011 (CUB)(Chen et al. 2019b), and CIFAR-FS(Bertinetto et al. 2018). |
| Dataset Splits | Yes | All datasets follow a standard division and all images are resized to predefined resslutions following standard settings. We evaluate the performance under standard 5-way-1-shot and 5-way-5-shot settings. |
| Hardware Specification | No | No specific hardware details (such as CPU, GPU models, or memory) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or specific library versions). |
| Experiment Setup | Yes | For the feature extractor, we consider three state-of-the-art backbones, S2M2 (Mangla et al. 2020), IE (Rizve et al. 2021) and FEAT (Ye et al. 2020). The number of neurons in different layers of Rank MLP is [6400, 1024, 512, 256, 1] for S2M2 and FEAT, and [16384, 1024, 512, 256, 1] for IE. We set the weight decay to 10^-6 and the momenta to 0.9. The learning rate is fixed at 0.0005 for both networks. Note that during the meta test stage, Rank DNN does not need to finetune on 1-shot, but on 5-shot, Rank MLP needs to be finetuned with the support set to get good performance, where we sample 100 triplets randomly in each mini-batch, and the parameters of Rank MLP is updated in 100 iterations. The learning rate is set to 0.01. Rank DNN is optimized with SGD. |