深度预测编码网络中的分类与重建过程：对抗者还是盟友？
Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies?
AI总结：
本研究通过设计类似自编码器的模型家族，发现深度预测编码网络中分类与重建过程在共享表示上相互削弱，扩大维度或增加复杂度可缓解权衡，挑战了现有假设。
摘要：
受预测编码启发的深度视觉计算网络在共享的中间层中整合了分类和重建过程。尽管通常假设这些过程之间存在协同作用，但这一假设尚未得到令人信服的证明。在本研究中，我们批判性地审视了分类与重建在深度学习架构中如何相互作用。我们的方法利用了一组特意设计的、类似于自编码器的模型架构家族，每个架构都配备有编码器、解码器以及具有不同模块和复杂度的分类头。我们细致地分析了分类驱动和重建驱动的信息在模型架构的共享潜在层中能够在多大程度上无缝共存。我们的发现凸显了一个重大挑战：分类驱动的信息会削弱中间层共享表示中重建驱动的信息，反之亦然。虽然扩大共享表示的维度或增加网络的复杂性可以缓解这种权衡效应，但我们的结果挑战了预测编码中的普遍假设，并为未来深度网络中预测编码概念的发展提供了指导。
英文摘要：
Predictive coding-inspired deep networks for visual computing integrate classification and reconstruction processes in shared intermediate layers. Although synergy between these processes is commonly assumed, it has yet to be convincingly demonstrated. In this study, we take a critical look at how classifying and reconstructing interact in deep learning architectures. Our approach utilizes a purposefully designed family of model architectures reminiscent of autoencoders, each equipped with an encoder, a decoder, and a classification head featuring varying modules and complexities. We meticulously analyze the extent to which classification- and reconstruction-driven information can seamlessly coexist within the shared latent layer of the model architectures. Our findings underscore a significant challenge: Classification-driven information diminishes reconstruction-driven information in intermediate layers' shared representations and vice versa. While expanding the shared representation's dimensions or increasing the network's complexity can alleviate this trade-off effect, our results challenge prevailing assumptions in predictive coding and offer guidance for future iterations of predictive coding concepts in deep networks.
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。
