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Found 38 papers, 19 papers with code
TSLANet: Rethinking Transformers for Time Series Representation Learning
Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications.
Improve Knowledge Distillation via Label Revision and Data Selection
In addition to the supervision of ground truth, the vanilla KD method regards the predictions of the teacher as soft labels to supervise the training of the student model.
K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data
To capture these dependencies, Graph Neural Networks (GNNs) have emerged as powerful tools, yet their effectiveness is restricted by the quality of graph construction from MTS data.
PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station
WiFi-based human pose estimation is a suitable method for monitoring power operations due to its low cost, device-free, and robustness to various illumination conditions. In this paper, a novel Channel State Information (CSI)-based pose estimation framework, namely PowerSkel, is developed to address these challenges.
SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both the local and global sensor levels.
Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition
To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena.
Graph-Aware Contrasting for Multivariate Time-Series Classification
As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data.
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT.
Unlimited Knowledge Distillation for Action Recognition in the Dark
To address this issue, we propose an unlimited knowledge distillation (UKD) in this paper.
Shuffled Differentially Private Federated Learning for Time Series Data Analytics
Specifically, we employ local differential privacy to extend the privacy protection trust boundary to the clients.
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data
The existing SFDA methods that are mainly designed for visual applications may fail to handle the temporal dynamics in time series, leading to impaired adaptation performance.
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data
More specifically, a feature-domain discriminator is employed to align teacher's and student's representations for universal knowledge transfer.
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning
From the perspective of intelligent wearable applications, the possibility of a comprehensive ECG interpretation algorithm based on single-lead ECGs is also confirmed.
Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation
We thus consider a more realistic \textit{Few-Shot Video-based Domain Adaptation} (FSVDA) scenario where we adapt video models with only a few target video samples.
Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling
Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH).
Label-efficient Time Series Representation Learning: A Review
The scarcity of labeled data is one of the main challenges of applying deep learning models on time series data in the real world.
Contrastive Domain Adaptation for Time-Series via Temporal Mixup
Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains.
Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
To tackle performance degradation and address concerns in high video annotation cost uniformly, the video unsupervised domain adaptation (VUDA) is introduced to adapt video models from the labeled source domain to the unlabeled target domain by alleviating video domain shift, improving the generalizability and portability of video models.
Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation
The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC).
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
Specifically, we propose time-series specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module.
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models.
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges
We believe that this technical review can help to promote a sustainable development of AI R&D activities for the research community.
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data as well as the recent methods specifically developed for time series data.
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition
Video-based Unsupervised Domain Adaptation (VUDA) methods improve the robustness of video models, enabling them to be applied to action recognition tasks across different environments.
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study
While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to challenging environments in real-world scenarios are still under-explored.
Self-supervised Autoregressive Domain Adaptation for Time Series Data
Second, we propose a novel autoregressive domain adaptation technique that incorporates temporal dependency of both source and target features during domain alignment.
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data
Our evaluation includes adaptations of state-of-the-art visual domain adaptation methods to time series data in addition to recent methods specifically developed for time series data.
Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment
Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios.
Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues
Finally, a Fusion Attention Block (FAB) is proposed to adaptively fuse the features from the two branches and generate an optimized colorization result.
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network
For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem.
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.
Time-Series Representation Learning via Temporal and Contextual Contrasting
In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.
Ranked #1 on
Recognizing And Localizing Human Actions
on HAR
Automatic Sleep Stage Classification
Contrastive Learning
+9
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.
Ranked #1 on
Automatic Sleep Stage Classification
on Sleep-EDF
Deep Inertial Odometry with Accurate IMU Preintegration
Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors.
Attention Sequence to Sequence Model for Machine Remaining Useful Life Prediction
Accurate estimation of remaining useful life (RUL) of industrial equipment can enable advanced maintenance schedules, increase equipment availability and reduce operational costs.
Hybrid coarse-fine classification for head pose estimation
In this paper, to do the estimation without facial landmarks, we combine the coarse and fine regression output together for a deep network.
Ranked #3 on
Head Pose Estimation
on AFLW
Deep Learning and Its Applications to Machine Health Monitoring: A Survey
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation.
