KernelWarehouse: Rethinking the Design of Dynamic Convolution

• URL: http://arxiv.org/abs/2406.07879v1
• Date: Wed, 12 Jun 2024 05:16:26 GMT
• Title: KernelWarehouse: Rethinking the Design of Dynamic Convolution
• Authors: Chao Li, Anbang Yao,
• Abstract summary: KernelWarehouse redefines the basic concepts of Kernels", assembling kernels" and attention function" We testify the effectiveness of KernelWarehouse on ImageNet and MS-COCO datasets using various ConvNet architectures.
• Score: 16.101179962553385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic convolution learns a linear mixture of n static kernels weighted with their input-dependent attentions, demonstrating superior performance than normal convolution. However, it increases the number of convolutional parameters by n times, and thus is not parameter efficient. This leads to no research progress that can allow researchers to explore the setting n>100 (an order of magnitude larger than the typical setting n<10) for pushing forward the performance boundary of dynamic convolution while enjoying parameter efficiency. To fill this gap, in this paper, we propose KernelWarehouse, a more general form of dynamic convolution, which redefines the basic concepts of ``kernels", ``assembling kernels" and ``attention function" through the lens of exploiting convolutional parameter dependencies within the same layer and across neighboring layers of a ConvNet. We testify the effectiveness of KernelWarehouse on ImageNet and MS-COCO datasets using various ConvNet architectures. Intriguingly, KernelWarehouse is also applicable to Vision Transformers, and it can even reduce the model size of a backbone while improving the model accuracy. For instance, KernelWarehouse (n=4) achieves 5.61%|3.90%|4.38% absolute top-1 accuracy gain on the ResNet18|MobileNetV2|DeiT-Tiny backbone, and KernelWarehouse (n=1/4) with 65.10% model size reduction still achieves 2.29% gain on the ResNet18 backbone. The code and models are available at https://github.com/OSVAI/KernelWarehouse.

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