receptive-field

问题 I am pretty new to artificial intelligence and neural networks. I have implemented a feed-forward neural network in PyTorch for classification on the MNIST data set. Now I want to visualize the receptive fields of (a subset of) hidden neurons. But I am having some problems with understanding the concept of receptive fields and when I google it all results are about CNNs. So can anyone help me with how I could do this in PyTorch and how to interpret the results? 回答1: I have previously

问题 There are many questions regarding the calculation of the receptive field. It is explained very well here on StackOverflow. However, there are no blogs or tutorials on how to calculate it in fully convolutional layer i.e. with residual blocks, feature map concatenation and upsampling layers (like feature pyramid network). To my understanding residual blocks and skip connections do not contribute to the receptive field and can be skipped. Answer from here. How are upsampling layers handled?

问题 I am pretty new to artificial intelligence and neural networks. I have implemented a feed-forward neural network in PyTorch for classification on the MNIST data set. Now I want to visualize the receptive fields of (a subset of) hidden neurons. But I am having some problems with understanding the concept of receptive fields and when I google it all results are about CNNs. So can anyone help me with how I could do this in PyTorch and how to interpret the results? 回答1: I have previously