Search Results for author: S. Y. Kung

Found 6 papers, 1 papers with code

Class-Discriminative CNN Compression

no code implementations21 Oct 2021 Yuchen Liu, David Wentzlaff, S. Y. Kung

We then propose a novel layer-adaptive hierarchical pruning approach, where we use a coarse class discrimination scheme for early layers and a fine one for later layers.

Evolving Transferable Pruning Functions

no code implementations21 Oct 2021 Yuchen Liu, S. Y. Kung, David Wentzlaff

Channel pruning has made major headway in the design of efficient deep learning models.

A compressive multi-kernel method for privacy-preserving machine learning

no code implementations20 Jun 2021 Thee Chanyaswad, J. Morris Chang, S. Y. Kung

Compressive Privacy is a privacy framework that employs utility-preserving lossy-encoding scheme to protect the privacy of the data, while multi-kernel method is a kernel based machine learning regime that explores the idea of using multiple kernels for building better predictors.

Activity Recognition Person Identification

Content-Aware GAN Compression

1 code implementation CVPR 2021 Yuchen Liu, Zhixin Shu, Yijun Li, Zhe Lin, Federico Perazzi, S. Y. Kung

We then propose a novel content-aware method to guide the processes of both pruning and distillation.

Image Generation Image Manipulation +1

Rethinking Class-Discrimination Based CNN Channel Pruning

no code implementations29 Apr 2020 Yuchen Liu, David Wentzlaff, S. Y. Kung

To this end, we initiate the first study on the effectiveness of a broad range of discriminant functions on channel pruning.

Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning

no code implementations24 Jul 2017 Artur Filipowicz, Thee Chanyaswad, S. Y. Kung

The quest for better data analysis and artificial intelligence has lead to more and more data being collected and stored.

General Classification

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