Comments (2)
Same question, how the model make sure that the attention layers capture the global information and the CNN layers capture local information with only one NLL loss? Have you figure it out?
as we know,the conventional attention module can capture features like fig 3.b(including diagonal and other positions). THIS ability is its nature,BUT i JUST wonder that when we add a branch that can capture local features,the attention module can not capture feature like before,i.g,(including diagonal and other positions),while it just capture global feature!!!
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Related Issues (20)
- Quantization HOT 1
- transfomer model with different paramters HOT 3
- Export model to ONNX HOT 1
- Error while evaluating model HOT 9
- Please share your quantization, quantization+pruning checkpoints HOT 1
- Missing Data Preparation section for the CNN / DailyMail dataset HOT 1
- Error while testing the model HOT 8
- Can not get the result as the paper if train the transformer from scratch. HOT 2
- How to measure the FLOPs/MACs? HOT 2
- in paragra 4 of HOT 1
- in the paragra 4 of paper HOT 1
- TransformerEncoderLayer HOT 4
- about kernel size HOT 1
- about dynamicconv_cuda HOT 1
- about padding!!! HOT 2
- About data ! HOT 1
- wmt16_en_de dataset link HOT 1
- model pruning
- Can‘t find the cnn branch,
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