18 citations · 27 across the 3 of their papers we have counts for
4 papers
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
Jihwan Bang, Hyunseo Koh, Seulki Park +3
Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. A large body of continual learning (CL) methods, h…
Rainbow Memory: Continual Learning with a Memory of Diverse Samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo +2
Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realisti…
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo +1
The cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognit…
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo +3
Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset…