# An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images

arXiv: https://arxiv.org/abs/1810.01163 · HTML: https://mlsanity.com/paper/1810.01163/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 11 of 11)

- [Unbalanced minibatch Optimal Transport; applications to Domain Adaptation](https://mlsanity.com/paper/2103.03606/) (2021)
- [Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport Distillation](https://mlsanity.com/paper/2112.09445/) (2021)
- [On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2207.13975/) (2022)
- [Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts](https://mlsanity.com/paper/2212.07328/) (2022)
- [AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation](https://mlsanity.com/paper/2403.01641/) (2024)
- [Dual-view Curricular Optimal Transport for Cross-lingual Cross-modal Retrieval](https://mlsanity.com/paper/2309.05451/) (2023)
- [A Novel Technique for Robust Training of Deep Networks With Multisource Weak Labeled Remote Sensing Data](https://mlsanity.com/paper/2510.05760/) (2025)
- [Computationally Efficient Wasserstein Loss for Structured Labels](https://mlsanity.com/paper/2103.00899/) (2021)
- [Deep Image Retrieval is not Robust to Label Noise](https://mlsanity.com/paper/2205.11195/) (2022)
- [A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition](https://mlsanity.com/paper/2112.05794/) (2021)
- [OT on the Map: Quantifying Domain Shifts in Geographic Space](https://mlsanity.com/paper/2604.16220/) (2026)

## Abstract (arXiv)

Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned to the availability of large number of accurately labeled samples. In practice, collecting large scale accurately labeled datasets is a challenging and tedious task in most scenarios of remote sensing image analysis, thus cheap surrogate procedures are employed to label the dataset. Training deep neural networks on such datasets with inaccurate labels easily overfits to the noisy training labels and degrades the performance of the classification tasks drastically. To mitigate this effect, we propose an original solution with entropic optimal transportation. It allows to learn in an end-to-end fashion deep neural networks that are, to some extent, robust to inaccurately labeled samples. We empirically demonstrate on several remote sensing datasets, where both scene and pixel-based hyperspectral images are considered for classification. Our method proves to be highly tolerant to significant amounts of label noise and achieves favorable results against state-of-the-art methods.