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MogaNet: Multi-order Gated Aggregation Network (ICLR 2024) We propose MogaNet, a new family of efficient ConvNets designed through the lens of multi-order game-theoretic interaction, to pursue informative context mining with preferable complexity-performance trade-offs It shows excellent scalability and attains competitive results among state-of-the-art models with more efficient use of model parameters on ImageNet and multifarious typical vision
[2211. 03295] MogaNet: Multi-order Gated Aggregation Network By contextualizing the kernel as global as possible, Modern ConvNets have shown great potential in computer vision tasks However, recent progress on multi-order game-theoretic interaction within deep neural networks (DNNs) reveals the representation bottleneck of modern ConvNets, where the expressive interactions have not been effectively encoded with the increased kernel size To tackle this