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Found 37 papers, 17 papers with code
Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI).
Self-supervised Learning for Electroencephalogram: A Systematic Survey
2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods.
Differentially Private Pre-Trained Model Fusion using Decentralized Federated Graph Matching
Model fusion is becoming a crucial component in the context of model-as-a-service scenarios, enabling the delivery of high-quality model services to local users.
GestureGPT: Zero-shot Interactive Gesture Understanding and Grounding with Large Language Model Agents
Current gesture recognition systems primarily focus on identifying gestures within a predefined set, leaving a gap in connecting these gestures to interactive GUI elements or system functions (e. g., linking a 'thumb-up' gesture to a 'like' button).
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources.
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation
Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction across numerous real-world tasks.
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
We propose DIVERSIFY, a general framework, for OOD detection and generalization on dynamic distributions of time series.
FedBone: Towards Large-Scale Federated Multi-Task Learning
The conflicting gradient projection technique is used to enhance the generalization of the large-scale general model between different tasks.
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning
With the constructed self-supervised learning task, DDLearn enlarges the data diversity and explores the latent activity properties.
FIXED: Frustratingly Easy Domain Generalization with Mixup
Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations.
Out-of-Distribution Representation Learning for Time Series Classification
Time series classification is an important problem in real world.
Domain-invariant Feature Exploration for Domain Generalization
Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i. e., the property within a domain, which is agnostic to other domains.
Domain Generalization for Activity Recognition via Adaptive Feature Fusion
To this end, we propose \emph{Adaptive Feature Fusion for Activity Recognition~(AFFAR)}, a domain generalization approach that learns to fuse the domain-invariant and domain-specific representations to improve the model's generalization performance.
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare.
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
Training on existing data often makes the model biased towards the distribution of the training data, thus the model might perform terribly on test data with different distributions.
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection
In this paper, we propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges and enhance the generalization ability in unsupervised anomaly detection.
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
There is a growing interest in applying machine learning techniques to healthcare.
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification
In this paper, we propose to view the time series classification problem from the distribution perspective.
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on HAR and HC datasets.
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare
The success of machine learning applications often needs a large quantity of data.
Generalizing to Unseen Domains: A Survey on Domain Generalization
Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.
Cross-domain Activity Recognition via Substructural Optimal Transport
In this paper, we propose substructure-level matching for domain adaptation (SSDA) to better utilize the locality information of activity data for accurate and efficient knowledge transfer.
Secure Weighted Aggregation for Federated Learning
The situation is exacerbated by the cloud-based implementation of digital services when user data are captured and stored in distributed locations, hence aggregation of the user data for ML could be a serious breach of privacy regulations.
Cryptography and Security Distributed, Parallel, and Cluster Computing
Learning to Match Distributions for Domain Adaptation
However, it remains challenging to determine which method is suitable for a given application since they are built with certain priors or bias.
FOCUS: Dealing with Label Quality Disparity in Federated Learning
It maintains a small set of benchmark samples on the FL server and quantifies the credibility of the client local data without directly observing them by computing the mutual cross-entropy between performance of the FL model on the local datasets and that of the client local FL model on the benchmark dataset.
Transfer Learning with Dynamic Adversarial Adaptation Network
In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions.
Transfer Learning with Dynamic Distribution Adaptation
Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions.
Ranked #7 on
Domain Adaptation
on ImageCLEF-DA
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
It is able to achieve accurate and personalized healthcare without compromising privacy and security.
A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning
Falls are one of the important causes of accidental or unintentional injury death worldwide.
Easy Transfer Learning By Exploiting Intra-domain Structures
In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance.
Ranked #4 on
Transfer Learning
on Office-Home
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning
In this paper, we propose a unified Transfer Channel Pruning (TCP) approach for accelerating UDA models.
Deep Transfer Learning for Cross-domain Activity Recognition
In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR).
Cross-Domain Activity Recognition
Human Activity Recognition
+1
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.
Ranked #1 on
Domain Adaptation
on Office-Caltech-10
Balanced Distribution Adaptation for Transfer Learning
To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution \underline{A}daptation~(BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA.
Cross-position Activity Recognition with Stratified Transfer Learning
STL consists of two components: Stratified Domain Selection (STL-SDS) can select the most similar source domain to the target domain; Stratified Activity Transfer (STL-SAT) is able to perform accurate knowledge transfer.
Stratified Transfer Learning for Cross-domain Activity Recognition
The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition.
Deep Learning for Sensor-based Activity Recognition: A Survey
This paper surveys the recent advance of deep learning based sensor-based activity recognition.
