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07/27/2023
EqGAN: Feature Equalization Fusion for Few-shot Image Generation
Due to the absence of fine structure and texture information, existing f...
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04/16/2023
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations
Autoencoders have achieved great success in various computer vision appl...
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02/16/2023
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Although Deep Learning (DL) has achieved success in complex Artificial I...
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01/28/2023
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
Since random initial models in Federated Learning (FL) can easily result...
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12/05/2022
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
Federated learning (FL) has been recognized as a privacy-preserving dist...
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08/16/2022
FedMR: Fedreated Learning via Model Recombination
As a promising privacy-preserving machine learning method, Federated Lea...
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05/24/2022
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment
Along with the popularity of Artificial Intelligence (AI) and Internet-o...
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05/10/2022
Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks
The 6G network enables a subnetwork-wide evolution, resulting in a "netw...
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05/09/2022
Model-Contrastive Learning for Backdoor Defense
Along with the popularity of Artificial Intelligence (AI) techniques, an...
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04/21/2022
Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation
Due to the prosperity of Artificial Intelligence (AI) techniques, more a...
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03/11/2022
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification
Adversarial attacks are often considered as threats to the robustness of...
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02/28/2022
Machine Learning Empowered Intelligent Data Center Networking: A Survey
To support the needs of ever-growing cloud-based services, the number of...
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02/23/2022
FedCAT: Towards Accurate Federated Learning via Device Concatenation
As a promising distributed machine learning paradigm, Federated Learning...
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01/29/2022
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications
As a promising method of central model training on decentralized device ...
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01/28/2022
O-ViT: Orthogonal Vision Transformer
Inspired by the tremendous success of the self-attention mechanism in na...
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12/27/2021
Learning Robust and Lightweight Model through Separable Structured Transformations
With the proliferation of mobile devices and the Internet of Things, dee...
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12/27/2021
ViR:the Vision Reservoir
The most recent year has witnessed the success of applying the Vision Tr...
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11/29/2021
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation
As a promising distributed machine learning paradigm, Federated Learning...
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06/28/2020
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications
Along with the proliferation of Artificial Intelligence (AI) and Interne...
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01/08/2020