通过梯度归一化缓解残差网络中的梯度重叠问题，提升非凸优化性能。
Mitigating Gradient Overlap in Deep Residual Networks with Gradient Normalization for Improved Non-Convex Optimization
- 引入Z-score归一化标准化各层梯度，减少跳接路径导致的梯度重叠。
- 在非凸优化场景下显著改善训练过程，避免权重更新过冲。
- 适用于大规模数据任务，对高精度模型训练有实际帮助。
深度学习中，残差网络（ResNets）有效缓解了梯度消失问题，使超深网络得以成功训练。然而，残差连接可能导致梯度重叠，即学习变换与跳接路径的梯度叠加，引发梯度估计过高。这种高估会导致优化效率下降，部分更新偏离最优区域。为此，本文研究了Z-score归一化（ZNorm）作为管理梯度重叠的方法。ZNorm通过调整梯度尺度，实现层间梯度标准化，降低重叠梯度的负面影响。实验表明，ZNorm在深度学习中常见的非凸优化场景下能显著改善训练过程，有助于更稳定地收敛。结果表明，该方法可优化梯度流动，在大规模数据处理任务中提升模型精度，对高要求应用场景具有实际价值。
原文摘要 · Abstract (English)
In deep learning, Residual Networks (ResNets) have proven effective in addressing the vanishing gradient problem, allowing for the successful training of very deep networks. However, skip connections in ResNets can lead to gradient overlap, where gradients from both the learned transformation and the skip connection combine, potentially resulting in overestimated gradients. This overestimation can cause inefficiencies in optimization, as some updates may overshoot optimal regions, affecting weight updates. To address this, we examine Z-score Normalization (ZNorm) as a technique to manage gradient overlap. ZNorm adjusts the gradient scale, standardizing gradients across layers and reducing the negative impact of overlapping gradients. Our experiments demonstrate that ZNorm improves training process, especially in non-convex optimization scenarios common in deep learning, where finding optimal solutions is challenging. These findings suggest that ZNorm can affect the gradient flow, enhancing performance in large-scale data processing where accuracy is critical.
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