# Deep multi-class learning from label proportions

arXiv: https://arxiv.org/abs/1905.12909 · HTML: https://mlsanity.com/paper/1905.12909/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 30 of 33)

- [Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training](https://mlsanity.com/paper/2102.08622/) (2021)
- [Learning from Label Proportions by Learning with Label Noise](https://mlsanity.com/paper/2203.02496/) (2022)
- [Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients](https://mlsanity.com/paper/2204.03304/) (2022)
- [Easy Learning from Label Proportions](https://mlsanity.com/paper/2302.03115/) (2023)
- [Two-stage Training for Learning from Label Proportions](https://mlsanity.com/paper/2105.10635/) (2021)
- [Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance](https://mlsanity.com/paper/2206.13079/) (2022)
- [In-Context Example Ordering Guided by Label Distributions](https://mlsanity.com/paper/2402.11447/) (2024)
- [PAC Learning Linear Thresholds from Label Proportions](https://mlsanity.com/paper/2310.10098/) (2023)
- [Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization](https://mlsanity.com/paper/2302.08947/) (2023)
- [MixBag: Bag-Level Data Augmentation for Learning from Label Proportions](https://mlsanity.com/paper/2308.08822/) (2023)
- [Learning from Partial Label Proportions for Whole Slide Image Segmentation](https://mlsanity.com/paper/2405.09041/) (2024)
- [Fast learning from label proportions with small bags](https://mlsanity.com/paper/2110.03426/) (2021)
- [Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery](https://mlsanity.com/paper/2208.11607/) (2022)
- [A Universal Unbiased Method for Classification from Aggregate Observations](https://mlsanity.com/paper/2306.11343/) (2023)
- [LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions](https://mlsanity.com/paper/2310.10096/) (2023)
- [Optimistic Rates for Learning from Label Proportions](https://mlsanity.com/paper/2406.00487/) (2024)
- [Disambiguation of weak supervision with exponential convergence rates](https://mlsanity.com/paper/2102.02789/) (2021)
- [Nearly Optimal Sample Complexity for Learning with Label Proportions](https://mlsanity.com/paper/2505.05355/) (2025)
- [Training image classifiers using Semi-Weak Label Data](https://mlsanity.com/paper/2103.10608/) (2021)
- [Learning from Label Proportions with Instance-wise Consistency](https://mlsanity.com/paper/2203.12836/) (2022)
- [Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions](https://mlsanity.com/paper/2401.11081/) (2024)
- [Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags](https://mlsanity.com/paper/2408.14130/) (2024)
- [Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions](https://mlsanity.com/paper/2411.10364/) (2024)
- [Label Differential Privacy via Aggregation](https://mlsanity.com/paper/2310.10092/) (2023)
- [Learning under Label Proportions for Text Classification](https://mlsanity.com/paper/2310.11707/) (2023)
- [Optimal Learning from Label Proportions with General Loss Functions](https://mlsanity.com/paper/2509.15145/) (2025)
- [Distributed LSTM-Learning from Differentially Private Label Proportions](https://mlsanity.com/paper/2301.07101/) (2023)
- [Evaluating LLP Methods: Challenges and Approaches](https://mlsanity.com/paper/2310.19065/) (2023)
- [FRACTAL: Fine-Grained Scoring from Aggregate Text Labels](https://mlsanity.com/paper/2404.04817/) (2024)
- [Class-aware and Augmentation-free Contrastive Learning from Label Proportion](https://mlsanity.com/paper/2408.06743/) (2024)

## Abstract (arXiv)

We propose a learning algorithm capable of learning from label proportions instead of direct data labels. In this scenario, our data are arranged into various bags of a certain size, and only the proportions of each label within a given bag are known. This is a common situation in cases where per-data labeling is lengthy, but a more general label is easily accessible. Several approaches have been proposed to learn in this setting with linear models in the multiclass setting, or with nonlinear models in the binary classification setting. Here we investigate the more general nonlinear multiclass setting, and compare two differentiable loss functions to train end-to-end deep neural networks from bags with label proportions. We illustrate the relevance of our methods on an image classification benchmark, and demonstrate the possibility to learn accurate image classifiers from bags of images.