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..
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
Authors: Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This meta-learning-based methodology significantly reduces the amount of unlabeled data needed for training and makes it more practical, as demonstrated in experiments. |
| Researcher Affiliation | Academia | 1National University of Defense Technology 2Hong Kong Baptist University 3University of Technology Sydney 4The University of Sydney 5RIKEN AIP 6The University of Texas at Austin 7The University of Tokyo |
| Pseudocode | Yes | Algorithm 1 Clustering-rule-aware task sampler (CATA); Algorithm 2 MM for NCDL.; Algorithm 3 MP for NCDL. |
| Open Source Code | Yes | To ensure the reproducibility of experimental results, we have provided codes of MM and MP at github.com/Haoang97/MEDI. |
| Open Datasets | Yes | We conduct experiments on four popular image classification benchmarks, including CIFAR-10 (Krizhevsky & Hinton, 2009), CIFAR-100 (Krizhevsky & Hinton, 2009), SVHN (Netzer et al., 2011), and Omni Glot (Lake et al., 2015). |
| Dataset Splits | No | The paper defines training and test sets for individual tasks within the meta-learning framework (Sl,tr i and Sl,ts i). However, it does not specify a separate, global validation dataset split for the entire experimental setup. |
| Hardware Specification | Yes | We implement all methods by Py Torch 1.7.1 and Python 3.7.6, and conduct all the experiments on two NVIDIA RTX 3090 GPUs. |
| Software Dependencies | Yes | We implement all methods by Py Torch 1.7.1 and Python 3.7.6, and conduct all the experiments on two NVIDIA RTX 3090 GPUs. |
| Experiment Setup | Yes | The output dimension of feature extractor πmm is set to dr = 512. The meta learning rate and inner learning are 0.4 and 0.001 respectively. We use a meta batch size (the amount of training tasks per training step) of 16\8 for {CIFAR-10,SVHN}\{CIFAR-100,Omniglot}. In addition, we choose k to be 10 which is suitable for all datasets. For each training task, we update the corresponding inner-algorithm by 10 steps. |