A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping

• URL: http://arxiv.org/abs/2005.11930v2
• Date: Wed, 10 Mar 2021 05:57:44 GMT
• Title: A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
• Authors: Benjamin Lucas, Charlotte Pelletier, Daniel Schmidt, Geoffrey I. Webb, and Fran\c{c}ois Petitjean
• Abstract summary: Sourcerer is a semi-supervised DA technique for producing land cover maps from SITS data. It takes a convolutional neural network trained on a source domain and then trains further on the available target domain. We show that Sourcerer outperforms all other methods for any quantity of labelled target data available.
• Score: 4.167265971166947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Land cover maps are a vital input variable to many types of environmental research and management. While they can be produced automatically by machine learning techniques, these techniques require substantial training data to achieve high levels of accuracy, which are not always available. One technique researchers use when labelled training data are scarce is domain adaptation (DA) -- where data from an alternate region, known as the source domain, are used to train a classifier and this model is adapted to map the study region, or target domain. The scenario we address in this paper is known as semi-supervised DA, where some labelled samples are available in the target domain. In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data. The technique takes a convolutional neural network trained on a source domain and then trains further on the available target domain with a novel regularizer applied to the model weights. The regularizer adjusts the degree to which the model is modified to fit the target data, limiting the degree of change when the target data are few in number and increasing it as target data quantity increases. Our experiments on Sentinel-2 time series images compare Sourcerer with two state-of-the-art semi-supervised domain adaptation techniques and four baseline models. We show that on two different source-target domain pairings Sourcerer outperforms all other methods for any quantity of labelled target data available. In fact, the results on the more difficult target domain show that the starting accuracy of Sourcerer (when no labelled target data are available), 74.2%, is greater than the next-best state-of-the-art method trained on 20,000 labelled target instances.

Related papers

• Robust Source-Free Domain Adaptation for Fundus Image Segmentation [3.585032903685044]
  Unlabelled Domain Adaptation (UDA) is a learning technique that transfers knowledge learned in the source domain from labelled data to the target domain with only unlabelled data. In this study, we propose a two-stage training stage for robust domain adaptation. We propose a novel robust pseudo-label and pseudo-boundary (PLPB) method, which effectively utilizes unlabeled target data to generate pseudo labels and pseudo boundaries.
  arXiv  Detail & Related papers  (2023-10-25T14:25:18Z)
• GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition [14.750765172614836]
  mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. To mitigate this issue, a novel self-aligned domain adaptation method called GaitSADA is proposed. Experiments show that GaitSADA outperforms representative domain adaptation methods with an improvement ranging from 15.41% to 26.32% on average accuracy in low data regimes.
  arXiv  Detail & Related papers  (2023-01-31T03:21:08Z)
• Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts [75.51482952586773]
  deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. We present a novel approach to geometric domain adaptation for image registration, adapting a model from a labeled source to an unlabeled target domain. Our method consistently improves on the baseline model by 50%/47% while even matching the accuracy of models trained on target data.
  arXiv  Detail & Related papers  (2022-07-01T12:16:42Z)
• ProCST: Boosting Semantic Segmentation using Progressive Cyclic Style-Transfer [38.03127458140549]
  We propose a novel two-stage framework for improving domain adaptation techniques. In the first step, we progressively train a multi-scale neural network to perform an initial transfer between the source data to the target data. This new data has a reduced domain gap from the desired target domain, and the applied UDA approach further closes the gap.
  arXiv  Detail & Related papers  (2022-04-25T18:01:05Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• Source-Free Open Compound Domain Adaptation in Semantic Segmentation [99.82890571842603]
  In SF-OCDA, only the source pre-trained model and the target data are available to learn the target model. We propose the Cross-Patch Style Swap (CPSS) to diversify samples with various patch styles in the feature-level. Our method produces state-of-the-art results on the C-Driving dataset.
  arXiv  Detail & Related papers  (2021-06-07T08:38:41Z)
• Distill and Fine-tune: Effective Adaptation from a Black-box Source Model [138.12678159620248]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge in previous related labeled datasets (source) to a new unlabeled dataset (target) We propose a novel two-step adaptation framework called Distill and Fine-tune (Dis-tune)
  arXiv  Detail & Related papers  (2021-04-04T05:29:05Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• Deep Domain-Adversarial Image Generation for Domain Generalisation [115.21519842245752]
  Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome this problem, domain generalisation (DG) methods aim to leverage data from multiple source domains so that a trained model can generalise to unseen domains. We propose a novel DG approach based on emphDeep Domain-Adversarial Image Generation (DDAIG)
  arXiv  Detail & Related papers  (2020-03-12T23:17:47Z)

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.