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.
no code implementations • 24 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.
no code implementations • 29 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.
no code implementations • 18 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.
no code implementations • 25 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.
Ranked #1 on Keyword Spotting on TAU Urban Acoustic Scenes 2019
no code implementations • 1 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.
no code implementations • 9 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.
no code implementations • 19 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.
no code implementations • 14 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.
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.
no code implementations • NeurIPS 2012 • Hyunsin Park, Sungrack Yun, Sanghyuk Park, Jongmin Kim, Chang D. Yoo
This paper describes a new acoustic model based on variational Gaussian process dynamical system (VGPDS) for phoneme classification.