44 citations · 90 across the 6 of their papers we have counts for
7 papers
TiDAL: Learning Training Dynamics for Active Learning
Seong Min Kye, Kwanghee Choi, Hyeongmin Byun +1
Active learning (AL) aims to select the most useful data samples from an unlabeled data pool and annotate them to expand the labeled dataset under a limited budget. Especially, unc…
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
Seong Min Kye, Kwanghee Choi, Joonyoung Yi +1
Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correcti…
Supervised attention for speaker recognition
Seong Min Kye, Joon Son Chung, Hoirin Kim
The recently proposed self-attentive pooling (SAP) has shown good performance in several speaker recognition systems. In SAP systems, the context vector is trained end-to-end toget…
Cross attentive pooling for speaker verification
Seong Min Kye, Yoohwan Kwon, Joon Son Chung
The goal of this paper is text-independent speaker verification where utterances come from 'in the wild' videos and may contain irrelevant signal. While speaker verification is nat…
Improving Multi-Scale Aggregation Using Feature Pyramid Module for Robust Speaker Verification of Variable-Duration Utterances
Youngmoon Jung, Seong Min Kye, Yeunju Choi +2
Currently, the most widely used approach for speaker verification is the deep speaker embedding learning. In this approach, we obtain a speaker embedding vector by pooling single-s…
Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs
Seong Min Kye, Youngmoon Jung, Hae Beom Lee +2
In practical settings, a speaker recognition system needs to identify a speaker given a short utterance, while the enrollment utterance may be relatively long. However, existing sp…
Meta-Learned Confidence for Few-shot Learning
Seong Min Kye, Hae Beom Lee, Hoirin Kim +1
Transductive inference is an effective means of tackling the data deficiency problem in few-shot learning settings. A popular transductive inference technique for few-shot metric-b…