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Results for different position of attention module in bottleneck.
Results for different position of attention module in bottleneck.
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Figshare
2022-08-02 更新
2026-04-28 收录
注意力机制优化
神经网络架构
数据链接:
https://figshare.com/articles/dataset/Results_for_different_position_of_attention_module_in_bottleneck_/20421462
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资源简介:
Results for different position of attention module in bottleneck.
应用场景:
创建时间:
2022-08-02
相关数据集
Model architectures used for the experiments.
神经网络架构
递归神经网络
STPNet is the model with short-term synaptic adaptation and RNN is the recurrent neural network. Convolutional layers are denoted as “conv-”. “maxpool” denotes max pooling using a 2x2 window and a str
NIAID Data Ecosystem
4
0
Ablations study results on different configurations of attention heads and reduction rates.
注意力机制优化
消融实验
Ablations study results on different configurations of attention heads and reduction rates.
NIAID Data Ecosystem
3
0
Average results of the parallel, sequential, and DNC variants–Fashion MNIST.
分类与聚类基准测试
神经网络架构
Average results of the parallel, sequential, and DNC variants–Fashion MNIST.
NIAID Data Ecosystem
0
0
Experimental results of the first improvement of the attention mechanism.
注意力机制优化
深度学习实验验证
Experimental results of the first improvement of the attention mechanism.
NIAID Data Ecosystem
3
0
Results of the integrating different attention modules on Lung-PET-CT-DX.
肺癌影像分析
注意力机制优化
Results of the integrating different attention modules on Lung-PET-CT-DX.
Figshare
2025-09-04 更新
3
0
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