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..
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 | Venue PDF | 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. |