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 this https URL

KernelWarehouse 是一种更通用的动态卷积形式，通过在同一层及相邻层之间利用卷积参数的依赖关系重新定义了 “核心”、“组装核心” 和 “注意函数” 等基本概念，以提高动态卷积的参数效率，并在 ImageNet 和 MS-COCO 数据集上使用各种 ConvNet 架构测试了其有效性。KernelWarehouse 还可应用于 Vision Transformers，在提高模型准确性的同时减小主干模型的尺寸。

KernelWarehouse: 动态卷积设计的重新思考

KernelWarehouse: Rethinking the Design of Dynamic Convolution

Dynamic convolution learns a linear mixture of $n$ static kernels weighted
with their sample-dependent attentions, demonstrating superior performance
compared to normal convolution. However, existing designs are
parameter-inefficient: they increase the number of convolutional parameters by
$n$ times. This and the optimization difficulty lead to no research progress in
dynamic convolution that can allow us to use a significant large value of $n$
(e.g., $n>100$ instead of typical setting $n<10$) to push forward the
performance boundary. In this paper, we propose $KernelWarehouse$, a more
general form of dynamic convolution, which can strike a favorable trade-off
between parameter efficiency and representation power. Its key idea is to
redefine the basic concepts of "$kernels$" and "$assembling$ $kernels$" in
dynamic convolution from the perspective of reducing kernel dimension and
increasing kernel number significantly. In principle, KernelWarehouse enhances
convolutional parameter dependencies within the same layer and across
successive layers via tactful kernel partition and warehouse sharing, yielding
a high degree of freedom to fit a desired parameter budget. We validate our
method on ImageNet and MS-COCO datasets with different ConvNet architectures,
and show that it attains state-of-the-art results. For instance, the
ResNet18|ResNet50|MobileNetV2|ConvNeXt-Tiny model trained with KernelWarehouse
on ImageNet reaches 76.05%|81.05%|75.52%|82.51% top-1 accuracy. Thanks to its
flexible design, KernelWarehouse can even reduce the model size of a ConvNet
while improving the accuracy, e.g., our ResNet18 model with 36.45%|65.10%
parameter reduction to the baseline shows 2.89%|2.29% absolute improvement to
top-1 accuracy.

通过重新定义动态卷积中的基本概念，并采用策略性的卷积核分割和仓库共享，提出了一种更高效的动态卷积方法 KernelWarehouse，实现了参数效率和表征能力的有利平衡，并通过在 ImageNet 和 MS-COCO 数据集上的实验证实了其在不同 ConvNet 架构上取得了最新的结果。