Activation prompts on intermediate layers outperform input-level visual prompting and parameter-efficient fine-tuning in accuracy and efficiency across 29 datasets.
Adversarial reprogramming of neural networks
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BlackVIP adapts foundation models via a Coordinator for input-dependent visual prompts and SPSA-GC for gradient estimation, enabling robust transfer on 19 datasets with low memory use and a link to randomized smoothing robustness.
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Visual prompting reimagined: The power of the Activation Prompts
Activation prompts on intermediate layers outperform input-level visual prompting and parameter-efficient fine-tuning in accuracy and efficiency across 29 datasets.
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Robust Adaptation of Foundation Models with Black-Box Visual Prompting
BlackVIP adapts foundation models via a Coordinator for input-dependent visual prompts and SPSA-GC for gradient estimation, enabling robust transfer on 19 datasets with low memory use and a link to randomized smoothing robustness.