递归块对角耦合用于视觉模型的资源高效训练
Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models
AI总结：
提出RBDC训练协议，通过递归方式以无参数块对角耦合独立训练的窄模型来构建宽模型，在ImageNet上使用DeiT和ResNet实现30% FLOPs降低且精度相当，并优于现有模型增长方法。
摘要：
从头训练高容量视觉模型需要大量计算资源。为了提高宽目标模型的训练效率，现有的增长方法通常假设存在更窄的模型，从而掩盖了整个流程的真实计算成本。我们提出了一种高效的训练协议RBDC，该协议通过递归方式以无参数块对角耦合独立训练的窄模型来构建宽模型。这允许灵活分配所有涉及模型的可用训练预算。在ImageNet上使用视觉变换器（DeiT）和卷积网络（ResNet）进行评估，我们的RBDC训练协议显示出比标准协议从头训练的模型更好的效率，在相似测试精度下实现了30%的FLOPs减少。与模型增长文献中的训练协议相比，它在相同训练FLOPs下也实现了更高的性能。最后，我们展示了我们的模型可以作为比原始模型更好的下游目标检测和实例分割任务的主干网络。
英文摘要：
Training high-capacity vision models from scratch requires substantial computational resources. To improve training efficiency of a wide target model, existing growth methods often assume the availability of narrower models, obscuring the true computational cost of the entire pipeline. We propose an efficient training protocol, RBDC, that builds wide models by coupling in a parameter-free block-diagonal way narrower, independently trained models in a recursive way. This allows a flexible allocation of the training budget available across all the models involved. Evaluated with vision transformers (DeiT) and convolutional networks (ResNet) on ImageNet, our RBDC training protocol shows a much better efficiency than models trained from scratch with the standard protocol, yielding 30% FLOPs reduction at similar test accuracies. It also achieves higher performances at same training FLOPs than training protocols from the model growth literature. Finally, we show that our models can serve as better backbones than their original counterparts for downstream object detection and instance segmentation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
