KernelWarehouse: Rethinking the Design of Dynamic Convolution (2406.07879v1)

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.

• The paper introduces KernelWarehouse, a dynamic convolution framework that enhances ConvNet performance and parameter efficiency.
• It utilizes kernel partitioning, shared warehouse construction, and a contrasting-driven attention function to optimize large kernel sets.
• Experiments on ImageNet and MS-COCO show significant top-1 accuracy improvements compared to existing dynamic convolution methods.

An Expert Overview of "KernelWarehouse: Rethinking the Design of Dynamic Convolution"

The paper "KernelWarehouse: Rethinking the Design of Dynamic Convolution" introduces a novel approach to dynamic convolution, a mechanism known as KernelWarehouse, which targets both enhancing the performance of convolutional neural networks (ConvNets) and improving parameter efficiency. This research addresses the critical trade-off between increasing the dynamic convolution kernel numbers and maintaining parameter efficiency, especially in the context where kernel number n exceeds 100, an order significantly larger than the typically employed n < 10.

Methodological Advancements

KernelWarehouse redefines the traditional concepts associated with dynamic convolution, namely "kernels," "assembling kernels," and "attention function." The approach exploits convolutional parameter dependencies within the same layer and across neighboring layers of a ConvNet, allowing for an efficient exploration of larger kernel numbers while keeping parameter increments in check. The proposed framework notably departs from underperforming methods that fail to retain representation power when scaling up n.

The proposed methodology consists of three interconnected components:

1. Kernel Partition: A novel partitioning strategy that analyzes parameter dependencies within an individual convolutional layer, allowing for kernel definitions on a smaller scale.
2. Warehouse Construction-With-Sharing: This aspect involves cross-layer parameter dependency exploration, creating a shared kernel warehouse that supports multiple layers while minimizing parameter redundancy.
3. Contrasting-Driven Attention Function (CAF): A bespoke attention mechanism tailored to optimize the unique cross-layer kernel sharing strategy, overcoming inefficacies observed in standard attention functions.

The concept of a convolutional parameter budget is also introduced, enabling configuration flexibility to adhere to various model size constraints, demonstrating considerable scalability for KernelWarehouse.

Performance Analysis

KernelWarehouse's efficacy is rigorously evaluated on prominent datasets, ImageNet and MS-COCO, demonstrating across-the-board performance gains in top-1 accuracy on several ConvNet architectures. For instance, substantial top-1 accuracy improvements of 5.61% and 4.38% are observed on ResNet18 and DeiT-Tiny architectures respectively, with significant reductions in model size in certain scenarios.

The paper also asserts that KernelWarehouse effectively outperforms existing dynamic convolution standards like DY-Conv and ODConv in both traditional and advanced training settings. The implementation of KernelWarehouse on lightweight networks such as MobileNetV2 similarly yields favorable results, emphasizing its versatility across network scales.

Implications and Future Directions

Practically, KernelWarehouse promises enhanced deployment of ConvNets in memory and computation-constrained environments, such as mobile applications and edge computing, by providing a means to decouple model capacity from size. Theoretically, its introduction of a generalizable dynamic convolution concept opens pathways for research in both ConvNet architecture design and efficient ConvNet adaptation techniques in broader contexts.

In terms of potential enhancements, the paper discusses runtime model speed optimizations and integrates KernelWarehouse with other dynamic convolution models like ODConv to push the performance bounds further.

Overall, KernelWarehouse represents a significant methodological advancement that could influence the direction of future dynamic convolution research and the development of AI systems across various sectors.