Search Results for author:
Found 6 papers, 1 papers with code
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized.
Secure Vertical Federated Learning Under Unreliable Connectivity
Most work in privacy-preserving federated learning (FL) has focused on horizontally partitioned datasets where clients hold the same features and train complete client-level models independently.
Efficient Vertical Federated Learning with Secure Aggregation
The majority of work in privacy-preserving federated learning (FL) has been focusing on horizontally partitioned datasets where clients share the same sets of features and can train complete models independently.
Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders
To study which type of deep features is appropriate for MIM as a learning target, we propose a simple MIM framework with serials of well-trained self-supervised models to convert an Image to a feature Vector as the learning target of MIM, where the feature extractor is also known as a teacher model.
Attacking Adversarial Attacks as A Defense
In this work, we find that the adversarial attacks can also be vulnerable to small perturbations.
Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties.
Ranked #6 on
Cloud Removal
on SEN12MS-CR
