3 papers
cs.LG2025
No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks
Attila Szász, Balázs Bánhelyi, Márk Jelasity
The ultimate goal of verification is to guarantee the safety of deployed neural networks. Here, we claim that all the state-of-the-art verifiers we are aware of fail to reach this…
cs.LG2024
How not to Stitch Representations to Measure Similarity: Task Loss Matching versus Direct Matching
András Balogh, Márk Jelasity
Measuring the similarity of the internal representations of deep neural networks is an important and challenging problem. Model stitching has been proposed as a possible approach,…
cs.CV2024
Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off
Levente Halmosi, Bálint Mohos, Márk Jelasity
Machine learning models are vulnerable to tiny adversarial input perturbations optimized to cause a very large output error. To measure this vulnerability, we need reliable methods…