Addressing Feature Imbalance in Sound Source Separation

no code implementations • 11 Sep 2023 • Jaechang Kim, Jeongyeon Hwang, Soheun Yi, Jaewoong Cho, Jungseul Ok

Neural networks often suffer from a feature preference problem, where they tend to overly rely on specific features to solve a task while disregarding other features, even if those neglected features are essential for the task.

Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution

1 code implementation • 30 Oct 2021 • Jaechang Kim, Yunjoo Lee, Seunghoon Hong, Jungseul Ok

To obtain a continuous representation of audio and enable super resolution for arbitrary scale factor, we propose a method of implicit neural representation, coined Local Implicit representation for Super resolution of Arbitrary scale (LISA).

Audio Super-Resolution Self-Supervised Learning +1

Gradient Inversion with Generative Image Prior

1 code implementation • NeurIPS 2021 • Jinwoo Jeon, Jaechang Kim, Kangwook Lee, Sewoong Oh, Jungseul Ok

Federated Learning (FL) is a distributed learning framework, in which the local data never leaves clients devices to preserve privacy, and the server trains models on the data via accessing only the gradients of those local data.

Federated Learning