摘要:
paper import torch.nn as nn import torch class sMLPBlock(nn.Module): ''' 稀疏MLP 不是一个样本的所有特征通过全连接层 而是部分通过全连接层 ''' def __init__(self, W, H, channels): su 阅读全文
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paper 通过密集连接的小卷积核实现细节特征(高频特征提取)提取 import torch.nn as nn class Dense(nn.Module): def __init__(self, in_channels): super(Dense, self).__init__() # self. 阅读全文
摘要:
paper 可以借鉴的点:下采样和上次样 融合两个不同尺度特征图 from collections import OrderedDict import torch import torch.nn as nn import torch.nn.functional as F def BasicConv( 阅读全文