Search Results for author:
Found 10 papers, 2 papers with code
An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation
Additionally, the evidential fusion branch capitalizes on the complementary attributes of the first two branches and leverages an evidence-based Dempster-Shafer fusion strategy, supervised by more reliable and accurate pseudo-labels of unlabeled data.
Implicit Neural Feature Fusion Function for Multispectral and Hyperspectral Image Fusion
In this paper, inspired by previous work of MHIF task, we realize that HR-MSI could serve as a high-frequency detail auxiliary input, leading us to propose a novel INR-based hyperspectral fusion function named Implicit Neural Feature Fusion Function (INF).
Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net), including an evidential classifier and a vanilla classifier.
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs.
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion
Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability.
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns.
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
To this end, we develop an approach that can effectively take advantage of the sparsity of matrix operations in GCN inference to significantly reduce the computational overhead.
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns.
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
Energy harvesting (EH) technology that harvests energy from ambient environment is a promising alternative to batteries for powering those devices due to the low maintenance cost and wide availability of the energy sources.
NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks
Membership inference attacks (MIAs) against machine learning models can lead to serious privacy risks for the training dataset used in the model training.
