A Novel Technique for Robust Training of Deep Networks With Multisource Weak Labeled Remote Sensing Data

• URL: http://arxiv.org/abs/2510.05760v1
• Date: Tue, 07 Oct 2025 10:25:43 GMT
• Title: A Novel Technique for Robust Training of Deep Networks With Multisource Weak Labeled Remote Sensing Data
• Authors: Gianmarco Perantoni, Lorenzo Bruzzone,
• Abstract summary: deep networks require large amounts of training samples to obtain good generalization capabilities.<n>This is done by exploiting the transition matrices describing the statistics of the errors of each source.<n>The effectiveness of the proposed method is validated by experiments on different datasets.
• Score: 13.80382608774738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has gained broad interest in remote sensing image scene classification thanks to the effectiveness of deep neural networks in extracting the semantics from complex data. However, deep networks require large amounts of training samples to obtain good generalization capabilities and are sensitive to errors in the training labels. This is a problem in remote sensing since highly reliable labels can be obtained at high costs and in limited amount. However, many sources of less reliable labeled data are available, e.g., obsolete digital maps. In order to train deep networks with larger datasets, we propose both the combination of single or multiple weak sources of labeled data with a small but reliable dataset to generate multisource labeled datasets and a novel training strategy where the reliability of each source is taken in consideration. This is done by exploiting the transition matrices describing the statistics of the errors of each source. The transition matrices are embedded into the labels and used during the training process to weigh each label according to the related source. The proposed method acts as a weighting scheme at gradient level, where each instance contributes with different weights to the optimization of different classes. The effectiveness of the proposed method is validated by experiments on different datasets. The results proved the robustness and capability of leveraging on unreliable source of labels of the proposed method.

Related papers

• Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition [50.61991746981703]
  Current state-of-the-art LTSSL approaches rely on high-quality pseudo-labels for large-scale unlabeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels.
  arXiv  Detail & Related papers  (2024-10-08T15:06:10Z)
• DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery [17.690698736544626]
  Semi-supervised learning (SSL) aims to help reduce the cost of the manual labelling process by leveraging a substantial pool of unlabelled data.<n>Most existing SSL frameworks are too bulky to run efficiently on a GPU with limited memory.<n>We propose textitDiverseModel to explore and analyse different networks in parallel for SSL to increase the diversity of pseudo labels.
  arXiv  Detail & Related papers  (2023-11-22T22:20:10Z)
• Distribution Shift Matters for Knowledge Distillation with Webly Collected Images [91.66661969598755]
  We propose a novel method dubbed Knowledge Distillation between Different Distributions" (KD$3$) We first dynamically select useful training instances from the webly collected data according to the combined predictions of teacher network and student network. We also build a new contrastive learning block called MixDistribution to generate perturbed data with a new distribution for instance alignment.
  arXiv  Detail & Related papers  (2023-07-21T10:08:58Z)
• Exploring Data Redundancy in Real-world Image Classification through Data Selection [20.389636181891515]
  Deep learning models often require large amounts of data for training, leading to increased costs. We present two data valuation metrics based on Synaptic Intelligence and gradient norms, respectively, to study redundancy in real-world image data. Online and offline data selection algorithms are then proposed via clustering and grouping based on the examined data values.
  arXiv  Detail & Related papers  (2023-06-25T03:31:05Z)
• Trustable Co-label Learning from Multiple Noisy Annotators [68.59187658490804]
  Supervised deep learning depends on massive accurately annotated examples. A typical alternative is learning from multiple noisy annotators. This paper proposes a data-efficient approach, called emphTrustable Co-label Learning (TCL)
  arXiv  Detail & Related papers  (2022-03-08T16:57:00Z)
• Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation [63.24594955429465]
  Multi-source entity linkage is critical in high-impact applications such as data cleaning and user stitching. AdaMEL is a deep transfer learning framework that learns generic high-level knowledge to perform multi-source entity linkage. Our framework achieves state-of-the-art results with 8.21% improvement on average over methods based on supervised learning.
  arXiv  Detail & Related papers  (2021-10-27T15:20:41Z)
• PseudoSeg: Designing Pseudo Labels for Semantic Segmentation [78.35515004654553]
  We present a re-design of pseudo-labeling to generate structured pseudo labels for training with unlabeled or weakly-labeled data. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes.
  arXiv  Detail & Related papers  (2020-10-19T17:59:30Z)
• Adversarial Knowledge Transfer from Unlabeled Data [62.97253639100014]
  We present a novel Adversarial Knowledge Transfer framework for transferring knowledge from internet-scale unlabeled data to improve the performance of a classifier. An important novel aspect of our method is that the unlabeled source data can be of different classes from those of the labeled target data, and there is no need to define a separate pretext task.
  arXiv  Detail & Related papers  (2020-08-13T08:04:27Z)
• Learning across label confidence distributions using Filtered Transfer Learning [0.44040106718326594]
  We propose a transfer learning approach to improve predictive power in noisy data systems with large variable confidence datasets. We propose a deep neural network method called Filtered Transfer Learning (FTL) that defines multiple tiers of data confidence as separate tasks. We demonstrate that using FTL to learn stepwise, across the label confidence distribution, results in higher performance compared to deep neural network models trained on a single confidence range.
  arXiv  Detail & Related papers  (2020-06-03T21:00:11Z)
• GradMix: Multi-source Transfer across Domains and Tasks [33.98368732653684]
  GradMix is a model-agnostic method applicable to any model trained with gradient-based learning rule. We conduct MS-DTT experiments on two tasks: digit recognition and action recognition.
  arXiv  Detail & Related papers  (2020-02-09T02:10:22Z)
• Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning [5.1293809610257775]
  State-of-the-art, high capacity deep neural networks require large amounts of labelled training data. They are also highly susceptible to label errors in this data. We propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data.
  arXiv  Detail & Related papers  (2020-02-07T10:42:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.