VanillaNet：深度学习中极简主义的力量
VanillaNet: the Power of Minimalism in Deep Learning
AI总结：
本文提出VanillaNet，一种避免高深度、捷径和自注意力的极简神经网络架构，通过训练后剪除非线性激活函数恢复原始结构，在资源受限环境中实现高效部署，性能媲美著名深度神经网络和vision transformers。
摘要：
基础模型的核心是“多即不同”的哲学，这在计算机视觉和自然语言处理的惊人成功中得到了印证。然而，transformer模型的优化挑战与固有复杂性呼唤向简单性进行范式转变。在本研究中，我们引入了VanillaNet，一种在设计上追求优雅的神经网络架构。通过避免高深度、捷径以及自注意力等复杂操作，VanillaNet简洁明了却异常强大。每一层都被精心设计得紧凑直接，训练后剪除非线性激活函数以恢复原始架构。VanillaNet克服了固有复杂性的挑战，非常适合资源受限的环境。其易于理解且高度简化的架构为高效部署开启了新的可能性。大量实验表明，VanillaNet的性能与著名的深度神经网络和vision transformers不相上下，展示了深度学习中极简主义的力量。VanillaNet的这一愿景具有重新定义格局和挑战基础模型现状的巨大潜力，为优雅且有效的模型设计开辟了新路径。预训练模型和代码可在https://github.com/huawei-noah/VanillaNet和https://gitee.com/mindspore/models/tree/master/research/cv/vanillanet获取。
英文摘要：
At the heart of foundation models is the philosophy of "more is different", exemplified by the astonishing success in computer vision and natural language processing. However, the challenges of optimization and inherent complexity of transformer models call for a paradigm shift towards simplicity. In this study, we introduce VanillaNet, a neural network architecture that embraces elegance in design. By avoiding high depth, shortcuts, and intricate operations like self-attention, VanillaNet is refreshingly concise yet remarkably powerful. Each layer is carefully crafted to be compact and straightforward, with nonlinear activation functions pruned after training to restore the original architecture. VanillaNet overcomes the challenges of inherent complexity, making it ideal for resource-constrained environments. Its easy-to-understand and highly simplified architecture opens new possibilities for efficient deployment. Extensive experimentation demonstrates that VanillaNet delivers performance on par with renowned deep neural networks and vision transformers, showcasing the power of minimalism in deep learning. This visionary journey of VanillaNet has significant potential to redefine the landscape and challenge the status quo of foundation model, setting a new path for elegant and effective model design. Pre-trained models and codes are available at https://github.com/huawei-noah/VanillaNet and https://gitee.com/mindspore/models/tree/master/research/cv/vanillanet.
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。
