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Found 15 papers, 6 papers with code
Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval
Cutting-edge studies focus on achieving unsupervised CDR but typically assume that the category spaces across domains are identical, an assumption that is often unrealistic in real-world scenarios.
Phase-driven Domain Generalizable Learning for Nonstationary Time Series
Monitoring and recognizing patterns in continuous sensing data is crucial for many practical applications.
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued.
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection
Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and outliers across a range of applications.
Federated Continual Novel Class Learning
In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique.
A Survey of Federated Unlearning: A Taxonomy, Challenges and Future Directions
Achieving an optimal equilibrium among these facets is crucial for maintaining the effectiveness and usability of FL systems while adhering to privacy and security standards.
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation
Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects.
DEJA VU: Continual Model Generalization For Unseen Domains
To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains.
PolicyCleanse: Backdoor Detection and Mitigation for Competitive Reinforcement Learning
To ensure the security of RL agents against malicious backdoors, in this work, we propose the problem of Backdoor Detection in multi-agent RL systems, with the objective of detecting Trojan agents as well as the corresponding potential trigger actions, and further trying to mitigate their bad impact.
Federated Class-Incremental Learning
It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes.
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
Our NTL-based authorization approach instead provides data-centric protection, which we call applicability authorization, by significantly degrading the performance of the model on unauthorized data.
Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator
Data quantity and quality are crucial factors for data-driven learning methods.
Weak Adaptation Learning: Addressing Cross-Domain Data Insufficiency With Weak Annotator
Data quantity and quality are crucial factors for data-driven learning methods.
Addressing Class Imbalance in Federated Learning
Our experiments demonstrate the importance of acknowledging class imbalance and taking measures as early as possible in FL training, and the effectiveness of our method in mitigating the impact.
Eavesdrop the Composition Proportion of Training Labels in Federated Learning
Federated learning (FL) has recently emerged as a new form of collaborative machine learning, where a common model can be learned while keeping all the training data on local devices.
