DMN4: Few-Shot Learning via Discriminative Mutual Nearest Neighbor Neural Network
Authors: Yang Liu, Tu Zheng, Jie Song, Deng Cai, Xiaofei He1828-1836
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method outperforms the existing state-of-the-arts on both fine-grained and generalized datasets. |
| Researcher Affiliation | Collaboration | Yang Liu1, Tu Zheng1,2, Jie Song3, Deng Cai1,2, Xiaofei He1,2 1State Key Lab of CAD&CG, College of Computer Science, Zhejiang University 2Fabu Inc., Hangzhou, China 3Zhejiang University |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found. |
| Open Source Code | No | No explicit statement or link for the open-sourcing of the described methodology's code was found. |
| Open Datasets | Yes | mini Image Net (Vinyals et al. 2016) is a subset of Image Net containing randomly selected 100 classes... Caltech-UCSD Birds-200-2011 (CUB) (Wah et al. 2011)... meta-i Nat (Wertheimer and Hariharan 2019) |
| Dataset Splits | Yes | We follow the setup provided by Sachin and Hugo that takes 64, 16 and 20 classes for training, validation and evaluation respectively. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1) were mentioned. Only optimizers like Adam and SGD are noted. |
| Experiment Setup | Yes | We meta-train Conv-4 from scratch for 30 epochs by Adam optimizer with learning rate 1e-3 and decay 0.1 every 10 epochs. With regard to Res Net-12, we first pre-trained it like in the previous literature and then meta-train it by momentum SGD for 40 epochs. The learning rate in meta-training is set 5e-4 for Res Net-12 and decay 0.5 every 10 epochs. |