12 citations · 16 across the 7 of their papers we have counts for
7 papers
MIDAS: Misalignment-based Data Augmentation Strategy for Imbalanced Multimodal Learning
Seong-Hyeon Hwang, Soyoung Choi, Steven Euijong Whang
Multimodal models often over-rely on dominant modalities, failing to achieve optimal performance. While prior work focuses on modifying training objectives or optimization procedur…
GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning
Minsu Kim, Seong-Hyeon Hwang, Steven Euijong Whang
In the context of continual learning, acquiring new knowledge while maintaining previous knowledge presents a significant challenge. Existing methods often use experience replay te…
T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning
Seong-Hyeon Hwang, Minsu Kim, Steven Euijong Whang
We study model confidence calibration in class-incremental learning, where models learn from sequential tasks with different class sets. While existing works primarily focus on acc…
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks
Seong-Hyeon Hwang, Minsu Kim, Steven Euijong Whang
We study the problem of robust data augmentation for regression tasks in the presence of noisy data. Data augmentation is essential for generalizing deep learning models, but most…
Quilt: Robust Data Segment Selection against Concept Drifts
Minsu Kim, Seong-Hyeon Hwang, Steven Euijong Whang
Continuous machine learning pipelines are common in industrial settings where models are periodically trained on data streams. Unfortunately, concept drifts may occur in data strea…
RegMix: Data Mixing Augmentation for Regression
Seong-Hyeon Hwang, Steven Euijong Whang
Data augmentation is becoming essential for improving regression performance in critical applications including manufacturing, climate prediction, and finance. Existing techniques…
Inspector Gadget: A Data Programming-based Labeling System for Industrial Images
Geon Heo, Yuji Roh, Seonghyeon Hwang +2
As machine learning for images becomes democratized in the Software 2.0 era, one of the serious bottlenecks is securing enough labeled data for training. This problem is especially…