Jun Huan

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The University of Kansas ∙ Baidu, Inc.

AGAN: Towards Automated Design of Generative Adversarial Networks

Recent progress in Generative Adversarial Networks (GANs) has shown prom...
06/25/2019 ∙ by Hanchao Wang, et al. ∙ 14 ∙ share

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Fast Universal Style Transfer for Artistic and Photorealistic Rendering

Universal style transfer is an image editing task that renders an input ...
07/06/2019 ∙ by Jie An, et al. ∙ 6 ∙ share

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Discriminatory Transfer

We observe standard transfer learning can improve prediction accuracies ...
07/03/2017 ∙ by Chao Lan, et al. ∙ 0 ∙ share

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On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics

In cheminformatics, compound-target binding profiles has been a main sou...
04/03/2017 ∙ by Chao Lan, et al. ∙ 0 ∙ share

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Data Dropout: Optimizing Training Data for Convolutional Neural Networks

Deep learning models learn to fit training data while they are highly ex...
09/01/2018 ∙ by Tianyang Wang, et al. ∙ 0 ∙ share

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Instance-based Deep Transfer Learning

Deep transfer learning has acquired significant research interest. It ma...
09/08/2018 ∙ by Tianyang Wang, et al. ∙ 0 ∙ share

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Understanding Actors and Evaluating Personae with Gaussian Embeddings

Understanding narrative content has become an increasingly popular topic...
04/06/2018 ∙ by Hannah Kim, et al. ∙ 0 ∙ share

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Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent

We interpret the variational inference of the Stochastic Gradient Descen...
01/18/2019 ∙ by Wenqing Hu, et al. ∙ 0 ∙ share

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DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks

Transfer learning through fine-tuning a pre-trained neural network with ...
01/26/2019 ∙ by Xingjian Li, et al. ∙ 0 ∙ share

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An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity

Regularization of Deep Neural Networks (DNNs) for the sake of improving ...
02/03/2019 ∙ by Yingzhen Yang, et al. ∙ 0 ∙ share

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FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary

We present a novel method of compression of deep Convolutional Neural Ne...
02/08/2019 ∙ by Yingzhen Yang, et al. ∙ 0 ∙ share

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The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation

The randomness in Stochastic Gradient Descent (SGD) is considered to pla...
06/18/2019 ∙ by Jingfeng Wu, et al. ∙ 0 ∙ share

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NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks

The problem of explaining deep learning models, and model predictions ge...
09/10/2019 ∙ by Isaac Ahern, et al. ∙ 0 ∙ share

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Towards Making Deep Transfer Learning Never Hurt

Transfer learning have been frequently used to improve deep neural netwo...
11/18/2019 ∙ by Ruosi Wan, et al. ∙ 0 ∙ share

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SecureGBM: Secure Multi-Party Gradient Boosting

Federated machine learning systems have been widely used to facilitate t...
11/27/2019 ∙ by Zhi Fengy, et al. ∙ 0 ∙ share

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StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks

Neural Architecture Search (NAS) has been widely studied for designing d...
06/06/2019 ∙ by Jie An, et al. ∙ 0 ∙ share

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Ultrafast Photorealistic Style Transfer via Neural Architecture Search

The key challenge in photorealistic style transfer is that an algorithm ...
12/05/2019 ∙ by Jie An, et al. ∙ 0 ∙ share

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Parameter-Free Style Projection for Arbitrary Style Transfer

Arbitrary image style transfer is a challenging task which aims to styli...
03/17/2020 ∙ by Siyu Huang, et al. ∙ 0 ∙ share

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Jun Huan

Featured Co-authors

Bo Li

Jiebo Luo

Zhanxing Zhu

Dejing Dou

Boyang Li

Nebojsa Jojic

Yingzhen Yang

Haesun Park

Haoyi Xiong

Jinwen Ma

Siyu Huang

AGAN: Towards Automated Design of Generative Adversarial Networks

Fast Universal Style Transfer for Artistic and Photorealistic Rendering

Discriminatory Transfer

On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics

Data Dropout: Optimizing Training Data for Convolutional Neural Networks

Instance-based Deep Transfer Learning

Understanding Actors and Evaluating Personae with Gaussian Embeddings

Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent

DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks

An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity

FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary

The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation

NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks

Towards Making Deep Transfer Learning Never Hurt

SecureGBM: Secure Multi-Party Gradient Boosting

StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks

Ultrafast Photorealistic Style Transfer via Neural Architecture Search

Parameter-Free Style Projection for Arbitrary Style Transfer