Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers

Shaik, Riyaaz Uddien; Unni, Aiswarya; Zeng, Weiping

doi:10.3390/rs14225774

Cited by 5 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SSL is a learning strategy to improve the performance of deep neural networks by utilizing unlabeled samples when the amount of labeled data are sparse [13]. Current SSL methods can be categorized into four groups, including generative models [14], consistency regularization [15], graph neural network models (GNNs) [16], and pseudo-labeling [17].…”

Section: Semi-supervised Learningmentioning

confidence: 99%

Semi-Supervised Medical Image Classification with Pseudo Labels Using Coalition Similarity Training

Liu,

Ling,

Liu

2024

Mathematics

View full text Add to dashboard Cite

The development of medical image classification models necessitates a substantial number of labeled images for model training. In real-world scenarios, sample sizes are typically limited and labeled samples often constitute only a small portion of the dataset. This paper aims to investigate a collaborative similarity learning strategy that optimizes pseudo-labels to enhance model accuracy and expedite its convergence, known as the joint similarity learning framework. By integrating semantic similarity and instance similarity, the pseudo-labels are mutually refined to ensure their quality during initial training. Furthermore, the similarity score is utilized as a weight to guide samples away from misclassification predictions during the classification process. To enhance the model’s generalization ability, an adaptive consistency constraint is introduced into the loss function to improve performance on untrained datasets. The model achieved a satisfactory accuracy of 93.65% at 80% labeling ratio, comparable to supervised learning methods’ performance. Even with very low labeling ratio (e.g., 5%), the model still attained an accuracy of 74.28%. Comparison with other techniques such as Mean Teacher and FixMatch revealed that our approach significantly outperforms them in medical image classification tasks through improving accuracy by approximately 2%, demonstrating this framework’s leadership in medical image classification.

show abstract

Section: Semi-supervised Learningmentioning

confidence: 99%

Semi-Supervised Medical Image Classification with Pseudo Labels Using Coalition Similarity Training

Liu,

Ling,

Liu

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…Otgonbaatar proposed a parameterized quantum circuit (PQC) with only 17 quantum bits for classifying a two-label Sentinel RSI dataset. Quantum-based pseudo-labelling for hyperspectral imagery classification is demonstrated by Shaik [ 22 ]. Noisy intermediate-scale quantum (NISQ) devices are an advanced quantum computing technology providing solutions for large-scale and complex practical quantum computation, such as solving high-complexity problems or supporting ML algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In specific cases, traditional machine learning tasks can be improved with exponential acceleration when it operates on a quantum computer [18,19]. The Harrow-Hassidim-Lloyd (HHL) algorithm is a quantum computing that has been successfully implanted in conventional ML theories and topics, such as data mining, artificial neural networks, computational learning theory, etc., as shown in [20][21][22][23][24]. HHL-based algorithms are based on the quantum phase estimation algorithm, which operates in a high-depth quantum circuit [25].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Scene Classification in Hybrid Classical–Quantum Transferring CNN with Small Samples

Zhang,

Mi,

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples. When labeled samples are not sufficient, the most common solution is using pre-trained CNNs with a great deal of natural image datasets (e.g., ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical–quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on the open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical–quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proven that the proposed algorithm improves the classification accuracy while greatly decreasing the amount of model parameters and the sum of training data.

show abstract

Remote Sensing Image Scene Classification in Hybrid Classical-Quantum Transferring CNN with Small Samples

Zhang¹,

Mi²,

Yang³

et al. 2023

Preprint

View full text Add to dashboard Cite

Deep learning is improving by leaps and bounds in remote sensing images (RSIs) analysis, pre-trained convolutional neural networks (CNNs) have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, pre-trained CNNs require massive annotated data as samples for data training. When labeled samples are not sufficient, the most common solution is to the pre-trained CNNs using a great deal of natural image dataset (e.g. ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical-quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical-quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proved that the proposed algorithm improves the classification accuracy while greatly decreasing sum of model parameters and sum of training data.

show abstract

Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers

Cited by 5 publications

References 17 publications

Semi-Supervised Medical Image Classification with Pseudo Labels Using Coalition Similarity Training

Semi-Supervised Medical Image Classification with Pseudo Labels Using Coalition Similarity Training

Remote Sensing Image Scene Classification in Hybrid Classical–Quantum Transferring CNN with Small Samples

Remote Sensing Image Scene Classification in Hybrid Classical-Quantum Transferring CNN with Small Samples

Contact Info

Product

Resources

About