2022-06-28-bai22c.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification	Proceedings of the 39th International Conference on Machine Learning	Multi-label classification (MLC) is a prediction task where each sample can have more than one label. We propose a novel contrastive learning boosted multi-label prediction model based on a Gaussian mixture variational autoencoder (C-GMVAE), which learns a multimodal prior space and employs a contrastive loss. Many existing methods introduce extra complex neural modules like graph neural networks to capture the label correlations, in addition to the prediction modules. We find that by using contrastive learning in the supervised setting, we can exploit label information effectively in a data-driven manner, and learn meaningful feature and label embeddings which capture the label correlations and enhance the predictive power. Our method also adopts the idea of learning and aligning latent spaces for both features and labels. In contrast to previous works based on a unimodal prior, C-GMVAE imposes a Gaussian mixture structure on the latent space, to alleviate the posterior collapse and over-regularization issues. C-GMVAE outperforms existing methods on multiple public datasets and can often match other models’ full performance with only 50% of the training data. Furthermore, we show that the learnt embeddings provide insights into the interpretation of label-label interactions.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bai22c	0	{G}aussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification	1383	1398	1383-1398	1383	false	Bai, Junwen and Kong, Shufeng and Gomes, Carla P	given family Junwen Bai given family Shufeng Kong given family Carla P Gomes	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/bai22c/bai22c.pdf