A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series
School of Medicine, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）医学院）；Digital Department, China Unicom Global Co., Ltd（中国联通全球有限公司数字部）；University of the Chinese Academy of Sciences（中国科学院大学）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
In medical time series disease diagnosis, two key challenges are identified.First, the high annotation cost of medical data leads to overfitting in models trained on label-limited, single-center datasets. To address this, we propose incorporating external data from related tasks and leveraging AE-GAN to extract prior knowledge,providing valuable references for downstream tasks. Second, many existing studies employ contrastive learning to derive more generalized medical sequence representations for diagnostic tasks, usually relying on manually designed diverse positive and negative sample pairs.However, these approaches are complex, lack generalizability, and fail to adaptively capture disease-specific features across different conditions.To overcome this, we introduce LMCF (Learnable Multi-views Contrastive Framework), a framework that integrates a multi-head attention mechanism and adaptively learns representations from different views through inter-view and intra-view contrastive learning strategies.Additionally, the pre-trained AE-GAN is used to reconstruct discrepancies in the target data as disease probabilities, which are then integrated into the contrastive learning process.Experiments on three target datasets demonstrate that our method consistently outperforms seven other baselines, highlighting its significant impact on healthcare applications such as the diagnosis of myocardial infarction, Alzheimer's disease, and Parkinson's disease.
As a representative self-supervised method, contrastive learning has achieved great successes in unsupervised training of representations. It trains an encoder by distinguishing positive samples from negative ones given query anchors. These positive and negative samples play critical roles in defining the objective to learn the discriminative encoder, avoiding it from learning trivial features. While existing methods heuristically choose these samples, we present a principled method where both positive and negative samples are directly learnable end-to-end with the encoder. We show that the po…
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs
University of New South Wales（新南威尔士大学）；Yonsei University（延世大学）；RIKEN AIP（理化学研究所人工智能项目）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Online continual learning (online CL) studies the problem of learning sequential tasks from an online data stream without task boundaries, aiming to adapt to new data while alleviating catastrophic forgetting on the past tasks. This paper proposes a framework Contrastive Vision Transformer (CVT), which designs a focal contrastive learning strategy based on a transformer architecture, to achieve a better stability-plasticity trade-off for online CL. Specifically, we design a new external attention mechanism for online CL that implicitly captures previous tasks' information. Besides, CVT contain…
Self-supervised learning of graph neural networks (GNNs) aims to learn an accurate representation of the graphs in an unsupervised manner, to obtain transferable representations of them for diverse downstream tasks. Predictive learning and contrastive learning are the two most prevalent approaches for graph self-supervised learning. However, they have their own drawbacks. While the predictive learning methods can learn the contextual relationships between neighboring nodes and edges, they cannot learn global graph-level similarities. Contrastive learning, while it can learn global graph-level …
Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective
Science & Technology on Integrated Information System Laboratory, Institute of Software Chinese Academy of Sciences（中国科学院软件研究所综合信息系统技术国家级重点实验室）；State Key Laboratory of Intelligent Game（智能博弈国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Computer Science, Institute of Software Chinese Academy of Sciences（中国科学院软件研究所计算机科学国家重点实验室）
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE（机器学习系，Mohamed bin Zayed人工智能大学，阿布扎赫德，阿联酋）；JD Explore Academy, Beijing, China（京东探索研究院，北京，中国）；Department of Computer Science and Technology, National University of Defense Technology, Changsha, China（计算机科学与技术系，国防科技大学，长沙，中国）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China（计算机科学与技术学院，哈尔滨工业大学，深圳，中国）；Department of Computer Science, University of California, Los Angeles, USA（计算机科学系，加州大学洛杉矶分校，美国）
Existed pre-trained models have achieved state-of-the-art performance on various text classification tasks. These models have proven to be useful in learning universal language representations. However, the semantic discrepancy between similar texts cannot be effectively distinguished by advanced pre-trained models, which have a great influence on the performance of hard-to-distinguish classes. To address this problem, we propose a novel Contrastive Learning with Label Distance (CLLD) in this work. Inspired by recent advances in contrastive learning, we specifically design a classification met…
Facebook AI Research（脸书人工智能研究院）；Stanford University（斯坦福大学）
While contrastive approaches of self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing views from different data points (negative pairs), recent \emph{non-contrastive} SSL (e.g., BYOL and SimSiam) show remarkable performance {\it without} negative pairs, with an extra learnable predictor and a stop-gradient operation. A fundamental question arises: why do these methods not collapse into trivial representations? We answer this question via a simple theoretical study and propose a novel ap…
Institute of Signal Processing and System Theory, University of Stuttgart（斯图加特大学信号处理与系统理论研究所）
Deep convolutional neural networks have considerably improved state-of-the-art results for semantic segmentation. Nevertheless, even modern architectures lack the ability to generalize well to a test dataset that originates from a different domain. To avoid the costly annotation of training data for unseen domains, unsupervised domain adaptation (UDA) attempts to provide efficient knowledge transfer from a labeled source domain to an unlabeled target domain. Previous work has mainly focused on minimizing the discrepancy between the two domains by using adversarial training or self-training. Wh…
Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）；University of Technology Sydney（悉尼科技大学）；Google Research（谷歌研究院）；University of Washington（华盛顿大学）；University of Maryland（马里兰大学）
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to learn collaboratively without sharing their private data. However, excessive computation and communication demands pose challenges to current FL frameworks, especially when training large-scale models. To prevent these issues from hindering the deployment of FL systems, we propose a lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch. This leads us to a more practical FL problem…
Nanyang Technological University（南洋理工大学）；Baidu Research（百度研究院）；Harvard University（哈佛大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
Contrastive learning has been widely applied to graph representation learning, where the view generators play a vital role in generating effective contrastive samples. Most of the existing contrastive learning methods employ pre-defined view generation methods, e.g., node drop or edge perturbation, which usually cannot adapt to input data or preserve the original semantic structures well. To address this issue, we propose a novel framework named Automated Graph Contrastive Learning (AutoGCL) in this paper. Specifically, AutoGCL employs a set of learnable graph view generators orchestrated by a…
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
City University of Hong Kong（香港城市大学）；Huazhong University of Science and Technology（华中科技大学）；Tencent（腾讯）；Shenzhen Technology University（深圳技术大学）；Peking University（北京大学）；Shenzhen University（深圳大学）
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. Most existing UDA methods learn domain-invariant feature representations by minimizing feature distances across domains. In this work, we build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets. Exploring the same set of categories shared by both domains, we introduce a simple yet effective framework CDCL, for domain alignment. In particular, given an anchor image fro…
FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；WeChat, Tencent（腾讯微信事业部）；Zhongguancun Laboratory（中关村实验室）；School of Computer, Beihang University（北京航空航天大学计算机学院）
Massive rumors usually appear along with breaking news or trending topics, seriously hindering the truth. Existing rumor detection methods are mostly focused on the same domain, and thus have poor performance in cross-domain scenarios due to domain shift. In this work, we propose an end-to-end instance-wise and prototype-wise contrastive learning model with a cross-attention mechanism for cross-domain rumor detection. The model not only performs cross-domain feature alignment but also enforces target samples to align with the corresponding prototypes of a given source domain. Since target labe…
Revolutionizing Precise Low Back Pain Diagnosis via Contrastive Learning
Faculty of Computer Science and Engineering（计算机科学与工程学院）；Ho Chi Minh City University of Technology（胡志明市技术大学）；Vietnam National University (HCMUT-VNU)（越南国家大学（HCMUT-VNU））；National Taiwan University（国立台湾大学）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Notre Dame（圣母大学）
In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) …
End-to-end Learnable Clustering for Intent Learning in Recommendation
Ant Group（蚂蚁集团）；National University of Singapore（新加坡国立大学）；Westlake University（西湖大学）；Alibaba Group（阿里巴巴集团）；National University of Defense Technology（国防科技大学）；Zhejiang University（浙江大学）
University of Science and Technology of China（中国科学技术大学）；JD AI Research（京东人工智能研究院）
Self-supervised learning (SSL) has recently become the favorite among feature learning methodologies. It is therefore appealing for domain adaptation approaches to consider incorporating SSL. The intuition is to enforce instance-level feature consistency such that the predictor becomes somehow invariant across domains. However, most existing SSL methods in the regime of domain adaptation usually are treated as standalone auxiliary components, leaving the signatures of domain adaptation unattended. Actually, the optimal region where the domain gap vanishes and the instance level constraint that…
Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning
Department of Computer Science University of California Davis（加州大学戴维斯分校计算机科学系）；Department of Electrical Engineering and Computer Sciences University of California Berkeley（加州大学伯克利分校电气工程与计算机科学系）；Department of Mechanical and Aerospace Engineering University of California Davis（加州大学戴维斯分校机械与航空航天工程系）
Gaze on the Prize通过回报引导的对比学习，提升视觉强化学习的样本效率，解决ManiSkill3基准中难以学习的任务。
Achieving artificially intelligent-native wireless networks is necessary for the operation of future 6G applications such as the metaverse. Nonetheless, current communication schemes are, at heart, a mere reconstruction process that lacks reasoning. One key solution that enables evolving wireless communication to a human-like conversation is semantic communications. In this paper, a novel machine reasoning framework is proposed to pre-process and disentangle source data so as to make it semantic-ready. In particular, a novel contrastive learning framework is proposed, whereby instance and clus…
Graph augmentations are essential for graph contrastive learning. Most existing works use pre-defined random augmentations, which are usually unable to adapt to different input graphs and fail to consider the impact of different nodes and edges on graph semantics. To address this issue, we propose a framework called Hybrid Augmented Automated Graph Contrastive Learning (HAGCL). HAGCL consists of a feature-level learnable view generator and an edge-level learnable view generator. The view generators are end-to-end differentiable to learn the probability distribution of views conditioned on the …
KTH Royal Institute of Technology（瑞典皇家理工学院）；Technical University of Munich（慕尼黑工业大学）
Self-supervised Learning (SSL) aims at learning representations of objects without relying on manual labeling. Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs. A Siamese network, which relies on data augmentation, is the popular architecture used in these methods. However, these methods rely on heuristically crafted data augmentation techniques. Furthermore, they use either contrastive terms or other tricks (e.g., asymmetry) to avoid trivial solutions that can occur in Siamese networks. In this study, we prop…
This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition. c-siam is the first network that extracts high-level linguistic information from speech by matching outputs of two identical transformer encoders. It contains augmented and target branches which are trained by: (1) masking inputs and matching outputs with a contrastive loss, (2) incorporating a stop gradient operation on the target branch, (3) using an extra learnable transformation on the augmented branch, (4) introducing new temporal augment functions to p…
Molecular Graph Contrastive Learning with Line Graph
School of Statistics and Mathematics, Central University of Finance and Economics（中央财经大学统计与数学学院）；School of Journalism, Communication University of China（中国传媒大学新闻学院）
Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子与电气工程学院）；University College London, London, U.K.（伦敦大学学院）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent（腾讯）
Recently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. …
In the contrastive analysis (CA) setting, machine learning practitioners are specifically interested in discovering patterns that are enriched in a target dataset as compared to a background dataset generated from sources of variation irrelevant to the task at hand. For example, a biomedical data analyst may seek to understand variations in genomic data only present among patients with a given disease as opposed to those also present in healthy control subjects. Such scenarios have motivated the development of contrastive latent variable models to isolate variations unique to these target data…
