The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the
efficiency of training and inference for Transformer-based foundational models,
yielding promising results. However, the performance of SMoE heavily depends on
the choice of hyper-parameters, such as the number of experts and the number of
experts to be activated (referred to as top-k), resulting in significant
computational overhead due to the extensive model training by searching over
various hyper-parameter configurations. As a remedy, we introduce the Dynamic
Mixture of Experts (DynMoE) technique. DynMoE incorporates (1) a novel gating
method that enables each token to automatically determine the number of experts
to activate. (2) An adaptive process automatically adjusts the number of
experts during training. Extensive numerical results across Vision, Language,
and Vision-Language tasks demonstrate the effectiveness of our approach to
achieve competitive performance compared to GMoE for vision and language tasks,
and MoE-LLaVA for vision-language tasks, while maintaining efficiency by
activating fewer parameters. Our code is available at
this https URL

通过引入动态的专家混合技术（DynMoE），我们的方法在视觉、语言和视觉 - 语言任务中取得了竞争性能，同时通过激活更少的参数来保持效率。