Gated-SwinRMT unifies Swin windowed attention with retentive Manhattan decay via gating, reaching 80.22% top-1 accuracy on Mini-ImageNet versus 73.74% for the RMT baseline.
Learning multiple layers of fea- tures from tiny images
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Gated-SwinRMT: Unifying Swin Windowed Attention with Retentive Manhattan Decay via Input-Dependent Gating
Gated-SwinRMT unifies Swin windowed attention with retentive Manhattan decay via gating, reaching 80.22% top-1 accuracy on Mini-ImageNet versus 73.74% for the RMT baseline.