Unverified60% confidenceFactExact time
Cross-entropy loss often converges faster than MSE in classification tasks because, when paired with softmax output, gradients do not vanish in the saturation region.
2
Sources
60%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
梯度下降与反向传播详解：神经网络学习机制从零拆解
Related Claims
UnverifiedMSE suffers from gradient saturation when Sigmoid outputs are close to 0 or 1, causing training to be extremely slow, whereas cross-entropy has larger gradients when predictions deviate severely79% similarUnverifiedThe model uses class-weighted cross-entropy loss, assigning each class a weight inversely proportional to its frequency to mitigate data imbalance.72% similarUnverifiedForge性能比预期慢，主要瓶颈集中在交叉熵损失函数及其梯度计算和softmax上，且尚未实现KV-cache72% similarUnverifiedCLIP uses softmax cross-entropy loss with a computational complexity of O(n²), while SigLIP's sigmoid loss allows each image-text pair to be computed independently, eliminating the hard dependency on large-batch training.72% similarUnverifiedThere is a weak but consistent negative correlation between split entropy and node accuracy: the higher the entropy, the more likely the node judgment is to err.72% similar
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