Questions tagged [residual-networks]

For questions related to residual networks (ResNets), introduced in "Deep Residual Learning for Image Recognition" (2015) by Kaiming He et al. and that won the first place at "Large Scale Visual Recognition Challenge 2015" (ILSVRC2015).

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Understanding an extract on the motivation behind residual networks

I was reading about ResNets from this page, and I couldn't understand the following extract, about the motivation behind ResNets: "Since neural networks are good function approximators, they ...
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43 views

If the point of the ResNet skip connection is to let the main path learn the residual relative to identity, why are there convolutional skips?

In the original ResNet paper they talk about using plain identity skip connections when the input and output of a block have the same dimensions. When the input and output have different dimensions ...
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54 views

If vanishing gradients are NOT the problem that ResNets solve, then what is the explanation behind ResNet success?

I often see blog posts or questions on here starting with the premise that ResNets solve the vanishing gradient problem. The original 2015 paper contains the following passage in section 4.1: We ...
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42 views

Do deeper residual networks perform better or worse?

If you have an $18$ layer residual network versus and a $32$ layer residual network, why would the former do better at object detection than the latter, if you have both models are training using the ...
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2answers
71 views

What is the name of this neural network architecture with layers that are also connected to non-neighbouring layers?

Consider a feedforward neural network. Suppose you have a layer of inputs, which is feedforward to a hidden layer, and feedforward both the input and hidden layers to an output layer. Is there a name ...
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284 views

Why do ResNets avoid the vanishing gradient problem?

I read that, if we use the sigmoid or hyperbolic tangent activation functions in deep neural networks, we can have some problems with the vanishing of the gradient, and this is visible by the shapes ...
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1answer
719 views

Why are there transition layers in DenseNet?

The DenseNet architecture can be summarizde with this figure: Why there are transition layers between each block? In the papers, they justify the use of transition layers as follow : The ...
6
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1answer
480 views

Why aren't there neural networks that connect the output of each layer to all next layers?

Why aren't there neural networks that connect the output of each layer to all next layers? For example, the output of layer 1 would be fed to the input of layers 2, 3, 4, etc. Beyond computational ...
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1answer
101 views

Are neurons instantly feed forward when input arrives?

Let's say I have a neural network with 5 layers, including the input and output layer. Each layer has 5 nodes. Assume the layers are fully connected, but the 3rd node in the 2nd layer is connected to ...
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644 views

Should deep residual networks be viewed as an ensemble of networks?

The question is about the architecture of Deep Residual Networks (ResNets). The model that won the 1-st places at "Large Scale Visual Recognition Challenge 2015" (ILSVRC2015) in all five main tracks: ...