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cs.CV2025
Unlocking Noise-Resistant Vision: Key Architectural Secrets for Robust Models
Bum Jun Kim, Makoto Kawano, Yusuke Iwasawa +1
While the robustness of vision models is often measured, their dependence on specific architectural design choices is rarely dissected. We investigate why certain vision architectu…
cs.LG2025
Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections
Anastasis Kratsios, Bum Jun Kim, Takashi Furuya
Inspired by the Kolmogorov-Arnold superposition theorem, Kolmogorov-Arnold Networks (KANs) have recently emerged as an improved backbone for most deep learning frameworks, promisin…
cs.LG2025
Temperature-Free Loss Function for Contrastive Learning
Bum Jun Kim, Sang Woo Kim
As one of the most promising methods in self-supervised learning, contrastive learning has achieved a series of breakthroughs across numerous fields. A predominant approach to impl…