7 papers
Meta-Learning for Online Update of Recommender Systems
Minseok Kim, Hwanjun Song, Yooju Shin +3
Online recommender systems should be always aligned with users' current interest to accurately suggest items that each user would like. Since user interest usually evolves over tim…
AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queries
Woosung Choi, Minseok Kim, Marco A. Martínez Ramírez +2
This paper proposes a neural network that performs audio transformations to user-specified sources (e.g., vocals) of a given audio track according to a given description while pres…
Robust Learning by Self-Transition for Handling Noisy Labels
Hwanjun Song, Minseok Kim, Dongmin Park +2
Real-world data inevitably contains noisy labels, which induce the poor generalization of deep neural networks. It is known that the network typically begins to rapidly memorize fa…
LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation
Woosung Choi, Minseok Kim, Jaehwa Chung +1
Recent deep-learning approaches have shown that Frequency Transformation (FT) blocks can significantly improve spectrogram-based single-source separation models by capturing freque…
Investigating U-Nets with various Intermediate Blocks for Spectrogram-based Singing Voice Separation
Woosung Choi, Minseok Kim, Jaehwa Chung +2
Singing Voice Separation (SVS) tries to separate singing voice from a given mixed musical signal. Recently, many U-Net-based models have been proposed for the SVS task, but there w…
How does Early Stopping Help Generalization against Label Noise?
Hwanjun Song, Minseok Kim, Dongmin Park +1
Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels. In this paper, we claim that su…