Semi-Supervised Learning for Mars Imagery Classification

Wang, Wenjing; Lin, Lilang; Fan, Zejia; Liu, Jiaying

doi:10.1109/icip42928.2021.9506533

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…We note that other published techniques do outperform our model on the MSL v2.1 classification benchmark, but these approaches incorporate labels into a semi-supervised learning approach (95.86% test accuracy on MSL v2.1) [26] , or take a fully supervised approach with attention-based models (81.53% test accuracy on MSL v2.1) [3].…”

Section: Resultsmentioning

confidence: 88%

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Improving Contrastive Learning on Visually Homogeneous Mars Rover Images

Ward¹,

Moore²,

Pak³

et al. 2022

Preprint

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Contrastive learning has recently demonstrated superior performance to supervised learning, despite requiring no training labels. We explore how contrastive learning can be applied to hundreds of thousands of unlabeled Mars terrain images, collected from the Mars rovers Curiosity and Perseverance, and from the Mars Reconnaissance Orbiter. Such methods are appealing since the vast majority of Mars images are unlabeled as manual annotation is labor intensive and requires extensive domain knowledge. Contrastive learning, however, assumes that any given pair of distinct images contain distinct semantic content. This is an issue for Mars image datasets, as any two pairs of Mars images are far more likely to be semantically similar due to the lack of visual diversity on the planet's surface. Making the assumption that pairs of images will be in visual contrast -when they are in fact not -results in pairs that are falsely considered as negatives, impacting training performance. In this study, we propose two approaches to resolve this: 1) an unsupervised deep clustering step on the Mars datasets, which identifies clusters of images containing similar semantic content and corrects false negative errors during training, and 2) a simple approach which mixes data from different domains to increase visual diversity of the total training dataset. Both cases reduce the rate of false negative pairs, thus minimizing the rate in which the model is incorrectly penalized during contrastive training. These modified approaches remain fully unsupervised end-to-end. To evaluate their performance, we add a single linear layer trained to generate class predictions based on these contrastively-learned features and demonstrate increased performance compared to supervised models; observing an improvement in classification accuracy of 3.06% using only 10% of the labeled data.

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Section: Resultsmentioning

confidence: 88%

“…Semi-supervised learning for Mars images. Wang et al engineered a semi-supervised learning approach tailored for the semantic content of Mars rover images [26]. Their approach ignores problematic (redundant) training samples encountered during contrastive learning by making use of labels.…”

Section: Related Workmentioning

confidence: 99%

Improving Contrastive Learning on Visually Homogeneous Mars Rover Images

Ward¹,

Moore²,

Pak³

et al. 2022

Preprint

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“…with a single linear layer of 32-dimension. We implement all the algorithms based on USB (Wang, Chen, and Fan 2022) framework and use a single RTX 3090 GPU to train models. SGD is used to optimize parameters.…”

Section: Experiments Implement Detailsmentioning

confidence: 99%

BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning

Feng,

Xie,

Fang

et al. 2024

AAAI

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Semi-supervised Learning (SSL) reduces the need for extensive annotations in deep learning, but the more realistic challenge of imbalanced data distribution in SSL remains largely unexplored. In Class Imbalanced Semi-supervised Learning (CISSL), the bias introduced by unreliable pseudo-labels can be exacerbated by imbalanced data distributions. Most existing methods address this issue at instance-level through reweighting or resampling, but the performance is heavily limited by their reliance on biased backbone representation. Some other methods do perform feature-level adjustments like feature blending but might introduce unfavorable noise. In this paper, we discuss the bonus of a more balanced feature distribution for the CISSL problem, and further propose a Balanced Feature-Level Contrastive Learning method (BaCon). Our method directly regularizes the distribution of instances' representations in a well-designed contrastive manner. Specifically, class-wise feature centers are computed as the positive anchors, while negative anchors are selected by a straightforward yet effective mechanism. A distribution-related temperature adjustment is leveraged to control the class-wise contrastive degrees dynamically. Our method demonstrates its effectiveness through comprehensive experiments on the CIFAR10-LT, CIFAR100-LT, STL10-LT, and SVHN-LT datasets across various settings. For example, BaCon surpasses instance-level method FixMatch-based ABC on CIFAR10-LT with a 1.21% accuracy improvement, and outperforms state-of-the-art feature-level method CoSSL on CIFAR100-LT with a 0.63% accuracy improvement. When encountering more extreme imbalance degree, BaCon also shows better robustness than other methods.

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“…In recent years, applications of contrastive learning and transfer learning have been implemented for classification tasks in various domains. Wang et al developed a semisupervised learning framework for Mars imagery classification based on contrastive learning, demonstrating the applicability of contrastive methods in improving classification accuracy in planetary exploration scenarios [18]. Kato et al utilized contrastive learning for COVID-19 pneumonia classification from CT images, highlighting the efficacy of contrastive learning methods in training new classifiers following initial steps [19].…”

Section: Motivationmentioning

confidence: 99%

Renal Pathological Image Classification Based on Contrastive and Transfer Learning

Liu,

Zhu,

Tian

et al. 2024

Electronics

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Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of models being used for the analysis of renal pathological images. The diversity of renal pathological images and the imbalance between data acquisition and annotation have placed a significant burden on pathologists trying to perform reliable and timely analysis. Transfer learning based on contrastive pretraining is emerging as a viable solution to this dilemma. By incorporating unlabeled positive pretraining images and a small number of labeled target images, a transfer learning model is proposed for high-accuracy renal pathological image classification tasks. The pretraining dataset used in this study includes 5000 mouse kidney pathological images from the Open TG-GATEs pathological image dataset (produced by the Toxicogenomics Informatics Project of the National Institutes of Biomedical Innovation, Health, and Nutrition in Japan). The transfer training dataset comprises 313 human immunoglobulin A (IgA) chronic nephritis images collected at Fukushima Medical University Hospital. The self-supervised contrastive learning algorithm “Bootstrap Your Own Latent” was adopted for pretraining a residual-network (ResNet)-50 backbone network to extract glomerulus feature expressions from the mouse kidney pathological images. The self-supervised pretrained weights were then used for transfer training on the labeled images of human IgA chronic nephritis pathology, culminating in a binary classification model for supervised learning. In four cross-validation experiments, the proposed model achieved an average classification accuracy of 92.2%, surpassing the 86.8% accuracy of the original RenNet-50 model. In conclusion, this approach successfully applied transfer learning through mouse renal pathological images to achieve high classification performance with human IgA renal pathological images.

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Semi-Supervised Learning for Mars Imagery Classification

Cited by 13 publications

References 16 publications

Improving Contrastive Learning on Visually Homogeneous Mars Rover Images

Improving Contrastive Learning on Visually Homogeneous Mars Rover Images

BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning

Renal Pathological Image Classification Based on Contrastive and Transfer Learning

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