Does applying cross-modal token pruning from vision to audio Transformers degrade robustness to noise perturba

Abstract

Abstract: Vision Transformers (ViTs) have achieved state-of-the-art performance across various computer vision tasks, but their high computational cost remains a challenge. Token pruning has been proposed to reduce this cost by selectively removing less important tokens. While effective in vision tasks by discarding non-object regions, applying this technique to audio tasks presents unique challenges, as distinguishing relevant from irrelevant regions in time-frequency representations is less straightforward. In this study, for the first time, we applied token pruning to ViT-based audio classification m

Research Question

Does applying cross-modal token pruning from vision to audio Transformers degrade robustness to noise perturbations (e.g., on ESC-50) when optimizing for throughput on variable-length spectrograms?

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Correction Record

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Provenance