A new open-access landscape concept dataset enables the first application of Robust TCAV to deep learning species distribution models, validating predictions against expert knowledge and uncovering novel ecological associations for two aquatic insect groups.
In: International Conference on Learning Representations (2018)
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An asymmetric multi-level distillation framework lets a student ViT approximate clean-image representations from distorted inputs alone, outperforming prior methods on classification under distortions.
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A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models
A new open-access landscape concept dataset enables the first application of Robust TCAV to deep learning species distribution models, validating predictions against expert knowledge and uncovering novel ecological associations for two aquatic insect groups.
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Distilling Vision Transformers for Distortion-Robust Representation Learning
An asymmetric multi-level distillation framework lets a student ViT approximate clean-image representations from distorted inputs alone, outperforming prior methods on classification under distortions.