Search Results for author: Hyunsin Park

Found 11 papers, 0 papers with code

Progressive Random Convolutions for Single Domain Generalization

no code implementations CVPR 2023 Seokeon Choi, Debasmit Das, Sungha Choi, Seunghan Yang, Hyunsin Park, Sungrack Yun

Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains.

Domain Generalization Image Augmentation

Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification

no code implementations24 Jun 2022 Byeonggeun Kim, Seunghan Yang, Jangho Kim, Hyunsin Park, JunTae Lee, Simyung Chang

While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance normalization has been a promising way to get domain-invariant features.

Acoustic Scene Classification Domain Generalization +1

Towards Robust Domain Generalization in 2D Neural Audio Processing

no code implementations29 Sep 2021 Byeonggeun Kim, Seunghan Yang, Jangho Kim, Hyunsin Park, Jun-Tae Lee, Simyung Chang

While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance normalization has been a promising way to get domain-invariant features.

Acoustic Scene Classification Domain Generalization +3

Federated Learning of User Verification Models Without Sharing Embeddings

no code implementations18 Apr 2021 Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling

We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.

Federated Learning

SubSpectral Normalization for Neural Audio Data Processing

no code implementations25 Mar 2021 Simyung Chang, Hyoungwoo Park, Janghoon Cho, Hyunsin Park, Sungrack Yun, Kyuwoong Hwang

In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group.

Keyword Spotting

Secure Federated Learning of User Verification Models

no code implementations1 Jan 2021 Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling

We consider the problem of training User Verification (UV) models in federated setup, where the conventional loss functions are not applicable due to the constraints that each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.

Federated Learning

Federated Learning of User Authentication Models

no code implementations9 Jul 2020 Hossein Hosseini, Sungrack Yun, Hyunsin Park, Christos Louizos, Joseph Soriaga, Max Welling

In this paper, we propose Federated User Authentication (FedUA), a framework for privacy-preserving training of UA models.

Federated Learning Privacy Preserving +1

Meta-Learning via Feature-Label Memory Network

no code implementations19 Oct 2017 Dawit Mureja, Hyunsin Park, Chang D. Yoo

The feature memory is used to store the features of input data samples and the label memory stores their labels.

Meta-Learning

Early Improving Recurrent Elastic Highway Network

no code implementations14 Aug 2017 Hyunsin Park, Chang D. Yoo

Expanding on the idea of adaptive computation time (ACT), with the use of an elastic gate in the form of a rectified exponentially decreasing function taking on as arguments as previous hidden state and input, the proposed model is able to evaluate the appropriate recurrent depth for each input.

Human Activity Recognition Language Modelling

Face Alignment Using Cascade Gaussian Process Regression Trees

no code implementations CVPR 2015 Donghoon Lee, Hyunsin Park, Chang D. Yoo

Without increasing prediction time, the prediction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better generalization.

Face Alignment regression

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