91 citations · 255 across the 11 of their papers we have counts for
11 papers
Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Hwanjun Song, Minseok Kim, Jae-Gil Lee
Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these c…
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…
KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing
Minseok Kim, Woosung Choi, Jaehwa Chung +2
Recently, many methods based on deep learning have been proposed for music source separation. Some state-of-the-art methods have shown that stacking many layers with many skip conn…
Music Demixing Challenge 2021
Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich +6
Music source separation has been intensively studied in the last decade and tremendous progress with the advent of deep learning could be observed. Evaluation campaigns such as MIR…
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…
Learning from Noisy Labels with Deep Neural Networks: A Survey
Hwanjun Song, Minseok Kim, Dongmin Park +2
Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of…
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…
Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection
Hwanjun Song, Minseok Kim, Sundong Kim +1
The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch s…