4 citations · 4 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy
Dongmin Park, Seola Choi, Doyoung Kim +2
Data pruning, which aims to downsize a large training set into a small informative subset, is crucial for reducing the enormous computational costs of modern deep learning. Though…
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…
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…
MLAT: Metric Learning for kNN in Streaming Time Series
Dongmin Park, Susik Yoon, Hwanjun Song +1
Learning a good distance measure for distance-based classification in time series leads to significant performance improvement in many tasks. Specifically, it is critical to effect…