32 citations · 68 across the 13 of their papers we have counts for
8 papers · 1 filter
Re-evaluating Group Robustness via Adaptive Class-Specific Scaling
Seonguk Seo, Bohyung Han
Group distributionally robust optimization, which aims to improve robust accuracies -- worst-group and unbiased accuracies -- is a prominent algorithm used to mitigate spurious cor…
Revisiting Machine Unlearning with Dimensional Alignment
Seonguk Seo, Dongwan Kim, Bohyung Han
Machine unlearning, an emerging research topic focusing on compliance with data privacy regulations, enables trained models to remove the information learned from specific data. Wh…
Relaxed Contrastive Learning for Federated Learning
Seonguk Seo, Jinkyu Kim, Geeho Kim +1
We propose a novel contrastive learning framework to effectively address the challenges of data heterogeneity in federated learning. We first analyze the inconsistency of gradient…
Information-Theoretic Bias Reduction via Causal View of Spurious Correlation
Seonguk Seo, Joon-Young Lee, Bohyung Han
We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation, which is effective to identify the feature-level algorithmic…
Unsupervised Learning of Debiased Representations with Pseudo-Attributes
Seonguk Seo, Joon-Young Lee, Bohyung Han
Dataset bias is a critical challenge in machine learning since it often leads to a negative impact on a model due to the unintended decision rules captured by spurious correlations…
Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim +3
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to in…
Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
Woong-Gi Chang, Tackgeun You, Seonguk Seo +2
We propose a novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks. We aim to adapt to both domains by specializing ba…
Learning for Single-Shot Confidence Calibration in Deep Neural Networks through Stochastic Inferences
Seonguk Seo, Paul Hongsuck Seo, Bohyung Han
We propose a generic framework to calibrate accuracy and confidence of a prediction in deep neural networks through stochastic inferences. We interpret stochastic regularization us…