Unsupervised Meta-Learning Through Latent-Space Interpolation in Generative Models
Abstract
Several recently proposed unsupervised meta-learning approaches rely on synthetic meta-tasks created using techniques such as random selection, clustering and/or augmentation. In this work, we describe a novel approach that generates meta-tasks using generative models. The proposed family of algorithms generate pairs of in-class and out-of-class samples from the latent space in a principled way, allowing us to create synthetic classes forming the training and validation data of a meta-task. We find that the proposed approach, LAtent Space Interpolation Unsupervised Meta-learning (LASIUM), outperforms or is competitive with current unsupervised learning baselines on few-shot classification tasks on the most widely used benchmark datasets.
Cite
Text
Khodadadeh et al. "Unsupervised Meta-Learning Through Latent-Space Interpolation in Generative Models." International Conference on Learning Representations, 2021.Markdown
[Khodadadeh et al. "Unsupervised Meta-Learning Through Latent-Space Interpolation in Generative Models." International Conference on Learning Representations, 2021.](https://mlanthology.org/iclr/2021/khodadadeh2021iclr-unsupervised/)BibTeX
@inproceedings{khodadadeh2021iclr-unsupervised,
title = {{Unsupervised Meta-Learning Through Latent-Space Interpolation in Generative Models}},
author = {Khodadadeh, Siavash and Zehtabian, Sharare and Vahidian, Saeed and Wang, Weijia and Lin, Bill and Boloni, Ladislau},
booktitle = {International Conference on Learning Representations},
year = {2021},
url = {https://mlanthology.org/iclr/2021/khodadadeh2021iclr-unsupervised/}
}