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TL;DR: Deep networks have some issues that skip connections fix. To address this statement: As I understand Resnet has some identity mapping layers that their task is to create the output as the same as the input of the layer The residual blocks don't strictly learn the identity mapping. They are simply capable of learning such a mapping. That is, the ...


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Imagine a genie grants you three wishes. Because you are an ambitious deep learning researcher your first wish is a perfect solution for a 1000-layer NN for Image Net, which promptly appears on your laptop. Now a genie induced solution doesn't give you any intuition how it might be interpreted as an ensemble, but do you really believe that you need 1000 ...


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Before proceeding, it's important to note that ResNets, as pointed out here, were not introduced to specifically solve the VGP, but to improve learning in general. In fact, the authors of ResNet, in the original paper, noticed that neural networks without residual connections don't learn as well as ResNets, although they are using batch normalization, which, ...


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Actually, this already exists! I happened to make a presentation of a paper that talks about this topic. These networks are called DenseNets, which stands for densely connected convolutional networks. Just like in your question, within a dense block, the output of each layer is given as input to all subsequent layers. Put another way, in a normal feed-...


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They explained in the paper why they introduce residual blocks. They argue that it's easier to learn residual functions $F(x) = H(x) - x$ and then add them to the original representation $x$ to get hidden representation $H(x) = F(x) + x$ than it is to learn hidden representation $H(x)$ directly from original representation. That's the main reason and ...


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It is unclear what kind of network your are referring to, there is not a single neural-network model so conceivable both cases could exist and serve some purpose, yet if you are looking for one that emulates nature and real neurons, then you are missing at least 2 ingredients ( time and the mechanisms of resting potentials and refractory periods), which in ...


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As explained in this paper , the major benefit of identity mapping is that it enables backpropagation signal to reach from output (last) layers to input (first) layers. You can see on the paper at section 2 that it resolves vanishing gradient problem which arises in deeper networks.


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Such a network could be either a Residual Network or a Highway Network depending upon the underlying architecture of the skip layers. They are primarily used to to tackle the problem of vanishing gradients in very deep networks by reusing activations from a previous layer and passing them to adjacent layers (two or three skips away). Highway Network: (...


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This could be called a residual neural network (ResNet), which is a neural network with skip connections, that is, connections that skip layers. Here's a screenshot of a figure from the paper Deep Residual Learning for Image Recognition (2015), an important paper that shows the usefulness of these architectures.


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One can concatenate with the previous layer outputs as well, and this approach in pursued in DenseNets. A nice illustration, that compares difference between ResNets and DenseNets is presented below: As pointed in the other answer it will lead to an increase of computation cost, with the same number of channels (given all other properties of architecture ...


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Interesting question, I can come with 2 explanations why we don't initialize weights with 1 mean value : It may be easier for the network to learn identity function, but we may have a similar issue about not being able to learn comparison, comparison is quite an important reasoning in my opinion, this is why having negative weight values is important, and ...


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Can residual connections be beneficial when we have a small training dataset? The usual rule of data science investigations applies here: Try it, measure the results, then you will know. It is very hard to tell, a priori, whether a specific architectural or hyperparameter choice will impact the performance of a neural network on a given problem. In this ...


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The problem with certain activation functions, such as the sigmoid, is that they squash the input to a finite interval (i.e. they are sometimes classified as saturating activation functions). For example, the sigmoid function has codomain $[0, 1]$, as you can see from the illustration below. This property/behaviour can lead to the vanishing gradient problem ...


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Well, I found an answer that satisfies me. The zero-padded identity is not ideal. Suppose we're mapping from 64 channels to 128 channels. Then the zero-padded identity will map to an output where half of the channels are the same as the inputs, and the other half are all zeros. So that means the main path is learning a residual for half of the output ...


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Residual Network are usually deeper and hence take more time to train. EfficientNet are trying to tackle this. However, the latest advice show that the architecture tend to play a crucial role in the performance of an RL algorithm, which might motivate you to do this. There is recent work on Neural Architecture Search applied to RL tasks (cf https://arxiv....


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First a little intro, skip to the end for the straight answers: residual networks were proposed after observing that deeper models tend to perform worse than their shallow counterpart if we just keep adding hidden layers without applying any other change to the architecture, as we can see in the very first picture of the original paper. The reason of this ...


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Just by having more parameters, the deeper model has a higher capacity than the smaller one. This means that theoretically it can learn to extract more complex features from the data. Additionally, more layers means that the model can extract even higher-level features from the data. So, generally speaking, deeper models will most of the times outperform ...


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The point of DenseNet was to go as deep as ResNets, if not deeper, and keep multiple skip connections to preserve the gradient flow back better as well as to keep the earlier layers context (which prevents overfitting). With layers as deep as 120, having a single block being fully concatenated to all the previous ones would mean having a way large feature ...


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