Introduces Targeted Downstream-Agnostic Attack (TDAA) that uses a threat image as feature anchor and example-specific perturbations to achieve targeted attacks on unknown downstream tasks from pre-trained encoders.
Bootstrap your own latent-a new approach to self-supervised learning,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HyDeS introduces hyperspherical density shaping with a von Mises-Fisher estimator to create theoretically grounded self-supervised representations that focus on foreground features.
citing papers explorer
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Targeted Downstream-Agnostic Attack
Introduces Targeted Downstream-Agnostic Attack (TDAA) that uses a threat image as feature anchor and example-specific perturbations to achieve targeted attacks on unknown downstream tasks from pre-trained encoders.
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Self-Supervised Representation Learning via Hyperspherical Density Shaping
HyDeS introduces hyperspherical density shaping with a von Mises-Fisher estimator to create theoretically grounded self-supervised representations that focus on foreground features.