Recently, deep learning methods have attracted much attention in the field of polarimetric synthetic aperture radar (PolSAR) data interpretation and understanding. However, for supervised methods, it ...requires large-scale labeled data to achieve better performance, and getting enough labeled data is a time-consuming and laborious task. Aiming to obtain a good classification result with limited labeled data, we focus on learning discriminative high-level features between multiple representations, which we call mutual information. As PolSAR data have multi-modal representations, there should have strong similarity between multi-modal features of the same pixel. In addition, each pixel has its own unique geocoding and scattering information. Hence, every pixel has great difference from other pixels in a specific representation space. Based on the above observations, this article proposes a mutual information-based self-supervised learning (MI-SSL) model to learn an implicit representation from unlabeled data. In this article, the self-supervised learning idea is first applied to PolSAR data processing. Furthermore, a reasonable pretext task, which is suitable for PolSAR data, is designed to extract mutual information for classification tasks. Compared with the state-of-the-art classification methods, experimental results on four PolSAR data sets demonstrate that our MI-SSL model produces impressive overall accuracy with fewer labeled data.
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new ...prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM- embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life . All our models are available through https://github.com/agemagician/ProtTrans .
The fast development of self-supervised learning (SSL) lowers the bar learning feature representation from massive unlabeled data and has triggered a series of researches on change detection of ...remote sensing images. Challenges in adapting SSL from natural images classification to remote sensing images change detection arise from difference between the two tasks. The learned patch-level feature representations are not satisfying for the pixel-level precise change detection. In this article, we proposed a novel pixel-level self-supervised hyperspectral spatial-spectral feature understanding network (HyperNet) to accomplish pixelwise feature representation for effective hyperspectral change detection. Concretely, not patches but the whole images are fed into the network and the multitemporal spatial-spectral features are compared pixel by pixel. Instead of processing the 2-D imaging space and spectral response dimension in hybrid style, a powerful spatial-spectral attention module is put forward to explore the spatial correlation and discriminative spectral features of multitemporal hyperspectral images (HSIs), separately. Only the positive samples at the same location of bitemporal HSIs are created and forced to be aligned, aiming at learning the spectral difference-invariant features. Moreover, a new similarity loss function named focal cosine is proposed to solve the problem of imbalanced easy and hard positive samples comparison, where the weights of those hard samples are enlarged and highlighted to promote the network training. Six hyperspectral datasets have been adopted to test the validity and generalization of the proposed HyperNet. The extensive experiments demonstrate the superiority of HyperNet over the state-of-the-art algorithms on downstream hyperspectral change detection tasks.
Source-free unsupervised domain adaptation (SFUDA) aims to conduct prediction on the target domain by leveraging knowledge from the well-trained source model. Due to the absence of source data in the ...SFUDA setting, the existing methods mainly build the target classifier by fine-tuning the source model incorporated with empirical adaptation losses. Although these methods have achieved somewhat promising results, nearly all of them typically suffer from the closed-fitting dilemma that their models are dominantly affected by these easy-to-distinguish instances than those hard-to-distinguish ones, resulting from the absence of the labeled source data. To address aforementioned issues, we propose the Dipolar Confidence Learning (DCL) for SFUDA. Specifically, we conduct positive confidence learning on the samples with standard outputs to avoid overfitting of the model to these samples. In contrast, we perform negative confidence learning for the samples with abnormal outputs to optimize the complementary label, which forces the network to pay more attention to these confusing samples. Furthermore, to achieve more generalized domain alignment, both the confidence-based fuzzy mixup and rotation-based self-supervised learning are respectively constructed to boost the representation ability of the target model. Finally, extensive experiments are conducted to demonstrate the effectiveness and performance superiority of the proposed method.
Recognition and quantitative analytics of histopathological cells are the golden standard for diagnosing multiple cancers. Despite recent advances in deep learning techniques that have been widely ...investigated for the automated segmentation of various types of histopathological cells, the heavy dependency on specific histopathological image types with sufficient supervised annotations, as well as the limited access to clinical data in hospitals, still pose significant challenges in the application of computer-aided diagnosis in pathology. In this paper, we focus on the model generalization of cell segmentation towards cross-tissue histopathological images. Remarkably, a novel target-specific finetuning-based self-supervised domain adaptation framework is proposed to transfer the cell segmentation model to unlabeled target datasets, without access to source datasets and annotations. When performed on the target unlabeled histopathological image set, the proposed method only needs to tune very few parameters of the pre-trained model in a self-supervised manner. Considering the morphological properties of pathological cells, we introduce two constraint terms at both local and global levels into this framework to access more reliable predictions. The proposed cross-domain framework is validated on three different types of histopathological tissues, showing promising performance in self-supervised cell segmentation. Additionally, the whole framework can be further applied to clinical tools in pathology without accessing the original training image data. The code and dataset are released at: https://github.com/NeuronXJTU/SFDA-CellSeg.
•Developing an effective quantum self-supervised learning framework for time series data analysis.•Improving the learning capability of LSTM using quantum methods.•Identifying specific and effective ...time series data augmentation techniques for improved pretext learning.•Devising suitable quantum encoding technique specific for time series data in hand.•Improving the predictive learning ability for the downstream task in hand using transfer learning.
Technologies such as Artificial Intelligence, Machine Learning, and Internet of Things has made unobtrusive mental health monitoring a reality. Since, obtaining a large-scale labelled dataset for mental health conditions is a big challenge; the self-supervised contrastive learning frameworks are more suitable for developing such systems. This paper presents a novel Quantum Long Short-Term Memory (LSTM) based Contrastive Learning framework for continuous mental health monitoring by leveraging LSTM's strengths in time series data analytics aiding it with the benefits of quantum computation, contrastive learning, and transfer learning. In the pretext task of the contrastive learning framework, a quantum guided LSTM base-encoder is developed for effective representational learning. The learnt model is then fine-tuned by training it with a small labelled dataset to further enhance its prediction capability. Experiments were carried out on seven benchmark datasets related to mental health conditions. With the enhanced representational and prediction abilities, the proposed model has shown superior performance over traditional ones. On heart rate variability dataset collected from (Schmidt et al., 2018), it achieves the greatest F1-score of 0.99. The paired t-test at 95% confidence level demonstrates that the proposed model outperforms the other related models.
Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use ...the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.
CCGL: Contrastive Cascade Graph Learning Xu, Xovee; Zhou, Fan; Zhang, Kunpeng ...
IEEE transactions on knowledge and data engineering,
05/2023, Volume:
35, Issue:
5
Journal Article
Peer reviewed
Open access
Supervised learning, while prevalent for information cascade modeling, often requires abundant labeled data in training, and the trained model is not easy to generalize across tasks and datasets. It ...often learns task-specific representations, which can easily result in overfitting for downstream tasks. Recently, self-supervised learning is designed to alleviate these two fundamental issues in linguistic and visual tasks. However, its direct applicability for information cascade modeling, especially graph cascade related tasks, remains underexplored. In this work, we present Contrastive Cascade Graph Learning ( CCGL ), a novel framework for information cascade graph learning in a contrastive , self-supervised , and task-agnostic way. In particular, CCGL first designs an effective data augmentation strategy to capture variation and uncertainty by simulating the information diffusion in graphs. Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data. Third, CCGL learns a task-specific cascade model via fine-tuning using labeled data. Finally, to make the model transferable across datasets and cascade applications, CCGL further enhances the model via distillation using a teacher-student architecture. We demonstrate that CCGL significantly outperforms its supervised and semi-supervised counterparts for several downstream tasks.
Self-supervised learning (SSL) has gained wide-spread attention in the remote sensing (RS) and Earth observation (EO) communities owing to its ability to learn task-agnostic representations without ...human-annotated labels. Nevertheless, most existing RS SSL methods are limited to learning either global semantic separable or local spatial perceptible representations. We argue that this learning strategy is suboptimal in the realm of RS since the required representations for different RS downstream tasks are often varied and complex. In this study, we proposed a unified SSL framework that is better suited for RS image representation learning. The proposed SSL framework, contrastive mask image distillation (CMID), is capable of learning representations with both global semantic separability and local spatial perceptibility by combining contrastive learning (CL) with masked image modeling (MIM) in a self-distillation way. Furthermore, our CMID learning framework is architecture-agnostic, which is compatible with both convolutional neural networks (CNNs) and vision transformers (ViTs), allowing CMID to be easily adapted to a variety of deep learning (DL) applications for RS understanding. Comprehensive experiments have been carried out on four downstream tasks (i.e., scene classification, semantic segmentation, object detection, and change detection) and the results show that models pretrained using CMID achieve a better performance than other state-of-the-art SSL methods on multiple downstream tasks. The code and pretrained models will be made available at https://github.com/NJU-LHRS/official-CMIDhttps://github.com/NJU-LHRS/official-CMID to facilitate SSL research and speed up the development of RS images DL applications.
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