ParameterNet: Parameters Are All You Need

• URL: http://arxiv.org/abs/2306.14525v2
• Date: Sun, 14 Jan 2024 12:20:01 GMT
• Title: ParameterNet: Parameters Are All You Need
• Authors: Kai Han, Yunhe Wang, Jianyuan Guo, Enhua Wu
• Abstract summary: We introduce a novel design principle, termed Net, aimed at augmenting the number of parameters in large-scale visual pretraining models. We leverage dynamic convolutions to incorporate additional parameters into the networks with only a marginal rise in FLOPs. The Net approach allows low-FLOPs networks to take advantage of large-scale visual pretraining.
• Score: 50.150436250355945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The large-scale visual pretraining has significantly improve the performance of large vision models. However, we observe the \emph{low FLOPs pitfall} that the existing low-FLOPs models cannot benefit from large-scale pretraining. In this paper, we introduce a novel design principle, termed ParameterNet, aimed at augmenting the number of parameters in large-scale visual pretraining models while minimizing the increase in FLOPs. We leverage dynamic convolutions to incorporate additional parameters into the networks with only a marginal rise in FLOPs. The ParameterNet approach allows low-FLOPs networks to take advantage of large-scale visual pretraining. Furthermore, we extend the ParameterNet concept to the language domain to enhance inference results while preserving inference speed. Experiments on the large-scale ImageNet-22K have shown the superiority of our ParameterNet scheme. For example, ParameterNet-600M can achieve higher accuracy on ImageNet than the widely-used Swin Transformer (81.6\% \emph{vs.} 80.9\%) and has much lower FLOPs (0.6G \emph{vs.} 4.5G). In the language domain, LLaMA-1B enhanced with ParameterNet achieves 2\% higher accuracy over vanilla LLaMA. The code will be released at \url{https://parameternet.github.io/}.

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