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Found 12 papers, 4 papers with code
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.
Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features
In computer vision, multi-label recognition are important tasks with many real-world applications, but classifying previously unseen labels remains a significant challenge.
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.
Learning-based Framework for Sensor Fault-Tolerant Building HVAC Control with Model-assisted Learning
As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption.
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.
One for Many: Transfer Learning for Building HVAC Control
Traditional HVAC control methods are typically based on creating explicit physical models for building thermal dynamics, which often require significant effort to develop and are difficult to achieve sufficient accuracy and efficiency for runtime building control and scalability for field implementations.
Hashing-Based Undersampling Ensemble for Imbalanced Pattern Classification Problems
Samples in the majority class are divided into many subspaces by a hashing method.
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.
MaskPlus: Improving Mask Generation for Instance Segmentation
Instance segmentation is a promising yet challenging topic in computer vision.
