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 | Conference PDF | Archive PDF | Plain Text | 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. |