# Dual-Label Learning With Irregularly Present Labels

arXiv: https://arxiv.org/abs/2410.14380 · HTML: https://mlsanity.com/paper/2410.14380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Dual Learning for Machine Translation](https://mlsanity.com/paper/1611.00179/) (2016)
- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution](https://mlsanity.com/paper/2003.07018/) (2020)
- [LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition](https://mlsanity.com/paper/2008.03687/) (2020)
- [DARWIN Series: Domain Specific Large Language Models for Natural Science](https://mlsanity.com/paper/2308.13565/) (2023)
- [Dual Supervised Learning](https://mlsanity.com/paper/1707.00415/) (2017)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [An Effective Approach for Multi-label Classification with Missing Labels](https://mlsanity.com/paper/2210.13651/) (2022)
- [General Partial Label Learning via Dual Bipartite Graph Autoencoder](https://mlsanity.com/paper/2001.01290/) (2020)
- [Multi-Party Dual Learning](https://mlsanity.com/paper/2104.06677/) (2021)
- [Physical formula enhanced multi-task learning for pharmacokinetics prediction](https://mlsanity.com/paper/2404.10354/) (2024)

## Abstract (arXiv)

In multi-task learning, labels are often missing irregularly across samples, which can be fully labeled, partially labeled or unlabeled. The irregular label presence often appears in scientific studies due to experimental limitations. It triggers a demand for a new training and inference mechanism that could accommodate irregularly present labels and maximize their utility. This work focuses on the two-label learning task and proposes a novel training and inference framework, Dual-Label Learning (DLL). The DLL framework formulates the problem into a dual-function system, in which the two functions should simultaneously satisfy standard supervision, structural duality and probabilistic duality. DLL features a dual-tower model architecture that allows for explicit information exchange between labels, aimed at maximizing the utility of partially available labels. During training, missing labels are imputed as part of the forward propagation process, while during inference, labels are predicted jointly as unknowns of a bivariate system of equations. Our theoretical analysis guarantees the feasibility of DLL, and extensive experiments are conducted to verify that by explicitly modeling label correlation and maximizing label utility, our method makes consistently better prediction than baseline approaches by up to 9.6% gain in F1-score or 10.2% reduction in MAPE. Remarkably, DLL maintains robust performance at a label missing rate of up to 60%, achieving even better results than baseline approaches at lower missing rates down to only 10%.