FLaG: Frequency-Domain Latent Attention Gating for Token Aggregation in Neural Networks
Researchers propose FLaG, a plug-in module that uses frequency-domain transformations to improve how neural networks aggregate token representations into predictions. The method applies real FFT transforms, learnable latent queries, and channel-wise gating to enhance token pooling across multiple domains. The approach shows strongest improvements on antimicrobial peptide prediction and image classification tasks, with potential applications across machine learning domains.
FLaG is a novel token aggregation module designed to address a common computational bottleneck in neural networks that convert token-level representations to sample-level predictions. The method operates by transforming token representations into the frequency domain using real FFT, summarizing spectral components with learnable latent queries, applying channel-wise gating, and reconstructing enhanced tokens for final pooling. The researchers evaluated FLaG across diverse tasks: antimicrobial peptide activity prediction using ESM2, image classification on CIFAR-10/100 with ResNet18, and text classification on IMDB and GLUE with RoBERTa. Analysis reveals that low-frequency bands contribute most to predictions, while higher-frequency patterns are sample-specific; the gating mechanism acts as a shared spectral reweighting stage with cross-attention patterns that vary by sample. The authors provide source code, data, and supplementary materials for reproducibility.
What's missing
The paper does not discuss computational overhead or inference time comparisons with baseline pooling methods, which would be relevant for practical deployment considerations.
What different sources said
- arXiv cs.AICenter
Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation
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