Search Results for author:
Found 13 papers, 7 papers with code
A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier Models
With the rapid development of artificial intelligence (AI), especially in the medical field, the need for its explainability has grown.
Disentangling data distribution for Federated Learning
Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy.
Unlearning during Learning: An Efficient Federated Machine Unlearning Method
To facilitate the implementation of the right to be forgotten, the concept of federated machine unlearning (FMU) has also emerged.
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data
In this paper, we introduce a Federated Domain-specific Knowledge Transfer (FDKT) framework, which enables domain-specific knowledge transfer from LLMs to SLMs while preserving clients' data privacy.
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
Concerns about utility, privacy, and training efficiency in FL have garnered significant research attention.
3DFill:Reference-guided Image Inpainting by Self-supervised 3D Image Alignment
We then fill the hole in the target image with the contents of the aligned image.
A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning
In this work, we propose a Federated Hybrid Self-Supervised Learning framework, named FedHSSL, that utilizes cross-party views (i. e., dispersed features) of samples aligned among parties and local views (i. e., augmentation) of unaligned samples within each party to improve the representation learning capability of the VFL joint model.
Learning Quality-aware Dynamic Memory for Video Object Segmentation
However, they mainly focus on better matching between the current frame and the memory frames without explicitly paying attention to the quality of the memory.
Ranked #11 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
(using extra training data)
Real-time Human-Centric Segmentation for Complex Video Scenes
To alleviate this problem, we propose a mechanism named Inner Center Sampling to improve the accuracy of instance segmentation.
Ranked #4 on
Human Instance Segmentation
on OCHuman
PoseDet: Fast Multi-Person Pose Estimation Using Pose Embedding
This simple framework achieves an unprecedented speed and a competitive accuracy on the COCO benchmark compared with state-of-the-art methods.
THOR, Trace-based Hardware-adaptive layer-ORiented Natural Gradient Descent Computation
It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice.
Matrix optimization based Euclidean embedding with outliers
Euclidean embedding from noisy observations containing outlier errors is an important and challenging problem in statistics and machine learning.
