通过反馈机制提升神经网络的稳定性与泛化能力，尤其在数据少或噪声多时表现更优。
Deep Feedback Models
- 用微分方程建模递归反馈，通过指数衰减稳定训练过程。
- 在低数据和高噪声下，对象识别与分割任务均优于前馈网络。
- 适用于医学影像，对多种噪声具有鲁棒性，适合实际应用场景。
深度反馈模型（DFMs）是一类具有状态特性的新型神经网络，结合自下而上的输入与随时间演化的高层表征。该反馈机制为原本静态的架构引入动态性，使DFM能迭代优化内部状态，模拟生物决策的部分特征。我们将其建模为通过循环神经网络求解的微分方程，并利用指数衰减实现稳定收敛。为评估其有效性，我们在两个关键条件下测试：对噪声的鲁棒性与有限数据下的泛化能力。在物体识别与分割任务中，DFM在低数据或高噪声环境下持续优于前馈模型。此外，该方法可应用于医学影像领域，对多种噪声扰动保持鲁棒。这些结果凸显了反馈机制在实现稳定、鲁棒与泛化学习中的重要性。代码已公开于 https://github.com/DCalhas/deep_feedback_models。
原文摘要 · Abstract (English)
Deep Feedback Models (DFMs) are a new class of stateful neural networks that combine bottom up input with high level representations over time. This feedback mechanism introduces dynamics into otherwise static architectures, enabling DFMs to iteratively refine their internal state and mimic aspects of biological decision making. We model this process as a differential equation solved through a recurrent neural network, stabilized via exponential decay to ensure convergence. To evaluate their effectiveness, we measure DFMs under two key conditions: robustness to noise and generalization with limited data. In both object recognition and segmentation tasks, DFMs consistently outperform their feedforward counterparts, particularly in low data or high noise regimes. In addition, DFMs translate to medical imaging settings, while being robust against various types of noise corruption. These findings highlight the importance of feedback in achieving stable, robust, and generalizable learning. Code is available at https://github.com/DCalhas/deep_feedback_models.
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