most citedNEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CV2026

Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain

Annika Bätz, Pavel Klasek, Seo-Young Ham +3

Automated train operation on existing railway infrastructure requires robust camera-based perception, yet the railway domain lacks public benchmark suites with standardized evaluat…

cs.LG2023★ 3 cited

NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks

Seokil Ham, Jungwuk Park, Dong-Jun Han +1

While multi-exit neural networks are regarded as a promising solution for making efficient inference via early exits, combating adversarial attacks remains a challenging problem. I…