Kyle Min

2 papers indexed

arxivcs.CVcs.AI2026-07-07

RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations

Woo Jae Kim, Kyle Min, Suhyeon Ha, Joonsung Jeon, Sung-eui Yoon

Multi-perturbation adversarial training (MAT) aims to achieve robustness against multiple $\ell_p$ perturbations but suffers from robustness trade-offs between different threats. To address this, we employ a mixture of experts (MoE) to route different threats through distinct mod…

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arxivcs.LGcs.CV2026-06-29

Same Concept, Different Directions: Cross-Modal Feature Heterogeneity in Sparse Autoencoders

Chungpa Lee, Jihoon Kwon, Kyle Min, Jy-yong Sohn

Vision-language models map images and text into a joint embedding space. However, these embeddings often entangle multiple semantic features, which limits their interpretability and controllability. While sparse autoencoders have emerged as a useful tool for decomposing these emb…

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