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Dacheng Tao

Professor of Computer Science

University of Sydney

Australia

Research Interests

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About

Dr. Dacheng Tao is an Associate Professor at The University of Sydney, Australia. His research areas include federated learning, neural networks, and semantic scene completion, as demonstrated in his recent articles. He has contributed to advancements in blockchain-enabled frameworks and multi-class visual anomaly detection.

Articles (21)

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2023

Collaborators (5)

Masashi Sugiyama

University of Tokyo

JAPAN

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Zhe Chen

Lecturer

La Trobe University

AUSTRALIA

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David Wei

Full Professor

Fordham University

UNITED STATES

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Yonggang Wen

Professor

Nanyang Technological University (NTU)

SINGAPORE

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Dacheng Tao

Professor

University of Sydney

AUSTRALIA

View details

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Dacheng Tao

Research Interests

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About

Articles (21)

2D Semantic-Guided Semantic Scene Completion

Attentive Learning Facilitates Generalization of Neural Networks

BIT-FL: Blockchain-Enabled Incentivized and Secure Federated Learning Framework

Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval

DFedADMM: Dual Constraint Controlled Model Inconsistency for Decentralize Federated Learning

DGL-GAN: discriminator-guided GAN compression

ESceme: Vision-and-Language Navigation with Episodic Scene Memory

Estimating Per-Class Statistics for Label Noise Learning

Exploring plain ViT features for multi-class unsupervised visual anomaly detection

Learning from models beyond fine-tuning

Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning

General Class-Balanced Multicentric Dynamic Prototype Pseudo-Labeling for Source-Free Domain Adaptation

Generative adversarial physics-informed neural networks for solving forward and inverse problem with small labeled samples

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

Learning General and Specific Embedding with Transformer for Few-Shot Object Detection

Learning to Explore Sample Relationships

Manifold Based Multi-View K-Means

On Exploring the Potential of Quantum Auto-Encoder for Learning Quantum Systems

PointWavelet: Learning in Spectral Domain for 3-D Point Cloud Analysis

Federated Learning With Only Positive Labels by Exploring Label Correlations

GraMMaR: Ground-aware Motion Model for 3D Human Motion Reconstruction

Collaborators (5)

Masashi Sugiyama

Zhe Chen

David Wei

Yonggang Wen

Dacheng Tao

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