Search Results for author:
Found 8 papers, 2 papers with code
ChebMixer: Efficient Graph Representation Learning with MLP Mixer
In this paper, we present a novel architecture named ChebMixer, a newly graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens.
PAGE: Equilibrate Personalization and Generalization in Federated Learning
Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server).
Class Attention Transfer Based Knowledge Distillation
Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network.
BodyGAN: General-Purpose Controllable Neural Human Body Generation
This is because current methods mainly rely on a single pose/appearance model, which is limited in disentangling various poses and appearance in human images.
UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model
However, the popular DensePose-COCO dataset relies on a sophisticated manual annotation system, leading to severe limitations in acquiring the denser and more accurate annotated pose resources.
WAS-VTON: Warping Architecture Search for Virtual Try-on Network
Despite recent progress on image-based virtual try-on, current methods are constraint by shared warping networks and thus fail to synthesize natural try-on results when faced with clothing categories that require different warping operations.
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack
In MDP, we first propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model.
Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism
Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries.
