Jiangchao Yao

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University of Technology Sydney

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels

It is challenging to train deep neural networks robustly on the industri...
09/28/2018 ∙ by Bo Han, et al. ∙ 8 ∙ share

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Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

Learning with noisy labels, which aims to reduce expensive labors on acc...
03/06/2019 ∙ by Jiangchao Yao, et al. ∙ 2 ∙ share

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Deep Learning from Noisy Image Labels with Quality Embedding

There is an emerging trend to leverage noisy image datasets in many visu...
11/02/2017 ∙ by Jiangchao Yao, et al. ∙ 0 ∙ share

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Collaborative Learning for Weakly Supervised Object Detection

Weakly supervised object detection has recently received much attention,...
02/10/2018 ∙ by Jiajie Wang, et al. ∙ 0 ∙ share

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Degeneration in VAE: in the Light of Fisher Information Loss

Variational Autoencoder (VAE) is one of the most popular generative mode...
02/19/2018 ∙ by Huangjie Zheng, et al. ∙ 0 ∙ share

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Variational Composite Autoencoders

Learning in the latent variable model is challenging in the presence of ...
04/12/2018 ∙ by Jiangchao Yao, et al. ∙ 0 ∙ share

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Masking: A New Perspective of Noisy Supervision

It is important to learn classifiers under noisy labels due to their ubi...
05/21/2018 ∙ by Bo Han, et al. ∙ 0 ∙ share

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Understanding VAEs in Fisher-Shannon Plane

In information theory, Fisher information and Shannon information (entro...
07/10/2018 ∙ by Huangjie Zheng, et al. ∙ 0 ∙ share

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Variational Collaborative Learning for User Probabilistic Representation

Collaborative filtering (CF) has been successfully employed by many mode...
09/22/2018 ∙ by Kenan Cui, et al. ∙ 0 ∙ share

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How Does Disagreement Benefit Co-teaching?

Learning with noisy labels is one of the most important question in weak...
01/14/2019 ∙ by Xingrui Yu, et al. ∙ 0 ∙ share

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Node Attribute Generation on Graphs

Graph structured data provide two-fold information: graph structures and...
07/23/2019 ∙ by Xu Chen, et al. ∙ 0 ∙ share

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Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks

Low-dimensional embeddings of knowledge graphs and behavior graphs have ...
08/28/2019 ∙ by Yuting Ye, et al. ∙ 0 ∙ share

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Jiangchao Yao

Featured Co-authors

Rui Zhang

Masashi Sugiyama

Mingyuan Zhou

Xu Chen

Gang Niu

Ya Zhang

Jun Sun

Ivor W. Tsang

Bo Han

Siheng Chen

Chengqi Zhang

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels

Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

Deep Learning from Noisy Image Labels with Quality Embedding

Collaborative Learning for Weakly Supervised Object Detection

Degeneration in VAE: in the Light of Fisher Information Loss

Variational Composite Autoencoders

Masking: A New Perspective of Noisy Supervision

Understanding VAEs in Fisher-Shannon Plane

Variational Collaborative Learning for User Probabilistic Representation

How Does Disagreement Benefit Co-teaching?

Node Attribute Generation on Graphs

Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks