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cs.CV2024
LayerShuffle: Enhancing Robustness in Vision Transformers by Randomizing Layer Execution Order
Matthias Freiberger, Peter Kun, Anders Sundnes Løvlie +1
Due to their architecture and how they are trained, artificial neural networks are typically not robust toward pruning or shuffling layers at test time. However, such properties wo…
cs.CV2023
Fooling Contrastive Language-Image Pre-trained Models with CLIPMasterPrints
Matthias Freiberger, Peter Kun, Christian Igel +2
Models leveraging both visual and textual data such as Contrastive Language-Image Pre-training (CLIP), are the backbone of many recent advances in artificial intelligence. In this…