Search Results for author: Huan-Hsin Tseng

Found 11 papers, 4 papers with code

Quantum Federated Learning With Quantum Networks

no code implementations23 Oct 2023 Tyler Wang, Huan-Hsin Tseng, Shinjae Yoo

A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable information that is vulnerable to access by third parties.

Federated Learning Transfer Learning

Federated Quantum Machine Learning with Differential Privacy

no code implementations10 Oct 2023 Rod Rofougaran, Shinjae Yoo, Huan-Hsin Tseng, Samuel Yen-Chi Chen

The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data.

Binary Classification Federated Learning +2

INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization

no code implementations8 Sep 2023 Xi Yu, Huan-Hsin Tseng, Shinjae Yoo, Haibin Ling, Yuewei Lin

Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information.

Disentanglement Domain Generalization

UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation

2 code implementations28 Mar 2023 Dmitrii Torbunov, Yi Huang, Huan-Hsin Tseng, Haiwang Yu, Jin Huang, Shinjae Yoo, MeiFeng Lin, Brett Viren, Yihui Ren

An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner.

Image-to-Image Translation Translation

On the robustness of non-intrusive speech quality model by adversarial examples

no code implementations11 Nov 2022 Hsin-Yi Lin, Huan-Hsin Tseng, Yu Tsao

It has been shown recently that deep learning based models are effective on speech quality prediction and could outperform traditional metrics in various perspectives.

Boosting Self-Supervised Embeddings for Speech Enhancement

1 code implementation7 Apr 2022 Kuo-Hsuan Hung, Szu-Wei Fu, Huan-Hsin Tseng, Hsin-Tien Chiang, Yu Tsao, Chii-Wann Lin

We further study the relationship between the noise robustness of SSL representation via clean-noisy distance (CN distance) and the layer importance for SE.

Self-Supervised Learning Speech Enhancement

Toward Real-World Voice Disorder Classification

no code implementations5 Dec 2021 Heng-Cheng Kuo, Yu-Peng Hsieh, Huan-Hsin Tseng, Chi-Te Wang, Shih-Hau Fang, Yu Tsao

Conclusion: By deploying factorized convolutional neural networks and domain adversarial training, domain-invariant features can be derived for voice disorder classification with limited resources.

Classification Model Compression

Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport

1 code implementation NeurIPS 2021 Hsin-Yi Lin, Huan-Hsin Tseng, Xugang Lu, Yu Tsao

This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing.

Speech Enhancement Unsupervised Domain Adaptation

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