Search Results for author: Yufei Han

Found 8 papers, 1 papers with code

Learning to dehaze with polarization

no code implementations NeurIPS 2021 Chu Zhou, Minggui Teng, Yufei Han, Chao Xu, Boxin Shi

Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms.

Image Dehazing Single Image Dehazing

Attack Transferability Characterization for Adversarially Robust Multi-label Classification

1 code implementation29 Jun 2021 Zhuo Yang, Yufei Han, Xiangliang Zhang

We unveil how the transferability level of the attack determines the attackability of the classifier via establishing an information-theoretic analysis of the adversarial risk.

Adversarial Attack Classification +3

Characterizing the Evasion Attackability of Multi-label Classifiers

no code implementations17 Dec 2020 Zhuo Yang, Yufei Han, Xiangliang Zhang

Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic.

Multi-Label Learning

Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines

no code implementations19 Dec 2019 Giancarlo Fissore, Aurélien Decelle, Cyril Furtlehner, Yufei Han

In order to take full advantage of these dependencies we consider a purely probabilistic setting in which the features imputation and multi-label classification problems are jointly solved.

Classification General Classification +2

Prototypical Networks for Multi-Label Learning

no code implementations17 Nov 2019 Zhuo Yang, Yufei Han, Guoxian Yu, Qiang Yang, Xiangliang Zhang

We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.

Multi-Label Classification Multi-Label Learning

Robust Federated Training via Collaborative Machine Teaching using Trusted Instances

no code implementations8 May 2019 Yufei Han, Xiangliang Zhang

In our work, we propose a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers, to improve robustness of the federated training process against local data corruption.

Data Poisoning Federated Learning

Collaborative and Privacy-Preserving Machine Teaching via Consensus Optimization

no code implementations7 May 2019 Yufei Han, Yuzhe ma, Christopher Gates, Kevin Roundy, Yun Shen

To address these challenges, we formulate collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk.

Mini-Batch Spectral Clustering

no code implementations7 Jul 2016 Yufei Han, Maurizio Filippone

The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets.

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