newsReddit r/MachineLearningTrust 52 · CommunityPublished 2d agoLive · 5h ago
Revisiting the Efficient Channel Attention paper (2019, 12k citations) - the central hypothesis isn't quite right [D]
ECA was positioned as a successor to SE . The idea behind ECA is quite simple. Unlike SE which reduces the channel means into a smaller hidden layer, it directly uses a 1d convolution kernel on the channel means themselves, avoiding the need for dimensionality reduction. The results are undeniable: ECA is a clear improvement over SE. The authors claim that cross-c
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- PossiblePossibly related (embedding) · 52%ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers →
- PossiblePossibly related (embedding) · 50%ERank in Latent Space as an Image-Complexity and Richness Measure →
- PossiblePossibly related (embedding) · 49%Morphing into Hybrid Attention Models →
- PossiblePossibly related (embedding) · 49%Thresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement →
- PossiblePossibly related (embedding) · 46%Beyond the Hard Budget: Sparsity Regularizers for More Interpretable Top-k Sparse Autoencoders →
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paperELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training TransformerspaperERank in Latent Space as an Image-Complexity and Richness MeasurepaperMorphing into Hybrid Attention ModelspaperThresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image EnhancementpaperBeyond the Hard Budget: Sparsity Regularizers for More Interpretable Top-k Sparse Autoencoders
Related across the graph
paperERank in Latent Space as an Image-Complexity and Richness MeasurepaperELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training TransformerspaperMorphing into Hybrid Attention ModelspaperBeyond the Hard Budget: Sparsity Regularizers for More Interpretable Top-k Sparse AutoencoderspaperThresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement