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神经网络中的label smooth为什么没有火？
Many state-of-the-art models are trained with label smoothing, but the inductive bias provided by this technique is not well understood In this work, we have summarized and explained several behaviors observed while training deep neural networks with label smoothing
Pytorch的nn. CrossEntropyLoss ()的weight怎么使用？ - 知乎
Pytorch的nn CrossEntropyLoss ()的weight怎么使用？分割实验，label标注的0-3四类，0类的比重过大，1类其次，2，3类都很少，怎么使用loss的weight来减轻样本不平衡问题？ weight参数该… 显示全部关注者 48 被浏览
知乎 - 有问题，就会有答案
知乎 - 有问题，就会有答案
神经网络中的label smooth为什么没有火？
Label Smoothing起到的作用实际上是抑制了feature norm，此时网络输出的softmax prob永远无法达到设定的 1-α (K-1) ，loss曲面上不再存在平缓区域，处处都有较大的梯度指向各个类中心，所以特征会更加聚拢。 Label Smoothing和teacher-student learning关系很密切阅读全文若羽
论文笔记（十四）Revisit Knowledge Distillation: a Teacher . . .
一种无需教师的知识蒸馏框架（现在换名字了：Revisiting Knowledge Distillation via Label Smoothing Regularization 通过标签平滑正则进行知识蒸馏）摘要知识蒸馏的目的是从较大教师模型中提取知识，迁移到轻量级学生模型中。其有效性通常归功于教师模型提供的类别间相似性的独特信息，在这个意义上，只有
How to fix the error ValueError: Expected input batch_size (49) to . . .
-> 3479 return torch _C _nn cross_entropy_loss( 3480 input, 3481 target, 3482 weight, 3483 _Reduction get_enum(reduction), 3484 ignore_index, 3485 label_smoothing, 3486 ) ValueError: Expected input batch_size (49) to match target batch_size (64) What is the problem here and how to resolve it? I tried modifying dataloader
python - PyTorch - RuntimeError: Expected floating point type for . . .
╔══════════════════════════╦═══════════════════════════════╦════════════════════╦═════════════════════════╗ ║ Data type ║ dtype ║ CPU tensor ║ GPU tensor
Label Smoothing for sparse_categorical_crossentropy
Based on the Tensorflow Documentation, one can add label smoothing to categorical_crossentropy by adding label_smoothing argument My question is what about sparse categorical crossentropy loss