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..
Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning
Authors: Huiwon Jang, Hankook Lee, Jinwoo Shin
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate that Ps Co outperforms existing unsupervised meta-learning methods under various in-domain and cross-domain few-shot classification benchmarks. |
| Researcher Affiliation | Collaboration | AKorea Advanced Institute of Science and Technology (KAIST) BLG AI Research EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Pseudo-supervised Contrast (Ps Co): Py Torch-like Pseudocode |
| Open Source Code | Yes | The code is available at https://github.com/alinlab/Ps Co. |
| Open Datasets | Yes | Omniglot (Lake et al., 2011) is a 28 × 28 gray-scale dataset of 1623 characters with 20 samples each. Mini Image Net (Ravi & Larochelle, 2017) is an 84 × 84 resized subset of ILSVRC-2012 (Deng et al., 2009) with 600 samples each. |
| Dataset Splits | Yes | We split the dataset into 120, 100, and 323 classes for meta-training, meta-validation, and meta-test respectively. In addition, the 0, 90, 180, and 270 degrees rotated views for each class become the different categories. Thus, we have a total of 6492, 400, and 1292 classes for meta-training, meta-validation, and meta-test respectively. (Omniglot) |
| Hardware Specification | Yes | In our experiments, we mainly use NVIDIA GTX3090 GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not provide a specific version number. It also references 'torchvision (Paszke et al., 2019)' without a version. |
| Experiment Setup | Yes | We train our models via stochastic gradient descent (SGD) with a batch size of N = 256 for 400 epochs. Following Chen et al. (2020b); Chen & He (2021), we use an initial learning rate of 0.03 with the cosine learning schedule, τMo Co = 0.2, and a weight decay of 5 × 10−4. We use a queue size of M = 16384 since Omniglot (Lake et al., 2011) and mini Image Net (Ravi & Larochelle, 2017) has roughly 100k meta-training samples. |