Residual Block
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A Residual Block is a neural network block that ...
- See: Residual Neural Network (ResNet), Wide Residual Network (WRN), Multiscale Residual Network (MSRN).
References
2021
- https://en.wikipedia.org/wiki/Residual_neural_network
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Canonical form of a residual neural network. A layer ℓ − 1 is skipped over activation from ℓ − 2
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2018
- https://towardsdatascience.com/residual-blocks-building-blocks-of-resnet-fd90ca15d6ec
- QUOTE: ... In a network with residual blocks, each layer feeds into the next layer and directly into the layers about 2–3 hops away. That’s it. But understanding the intuition behind why it was required in the first place, why it is so important and how similar it looks to some other state of the art architectures is where we are going to focus on. There are more than one interpretations of why residual blocks are awesome and how & why they are one of the key ideas that can make a neural network show state of the art performances on wide range of tasks. Before diving into the details, here is a picture of how a residual block actually looks like. ...
- QUOTE: ... In a network with residual blocks, each layer feeds into the next layer and directly into the layers about 2–3 hops away. That’s it. But understanding the intuition behind why it was required in the first place, why it is so important and how similar it looks to some other state of the art architectures is where we are going to focus on. There are more than one interpretations of why residual blocks are awesome and how & why they are one of the key ideas that can make a neural network show state of the art performances on wide range of tasks. Before diving into the details, here is a picture of how a residual block actually looks like. ...
2018
- (Li et al., 2018) ⇒ Juncheng Li, Faming Fang, Kangfu Mei, and Guixu Zhang. (2018). “Multi-scale Residual Network for Image Super-Resolution.” In: Proceedings of 15th European Conference in Computer Vision (ECCV 2018).
- QUOTE: ... In this paper, we propose a novel multiscale residual network (MSRN) to fully exploit the image features, which outperform most of the state-of-the-art methods. Based on the residual block, we introduce convolution kernels of different sizes to adaptively detect the image features in different scales. Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB). ...
2016
- (Zagoruyko & Komodakis, 2016) ⇒ Sergey Zagoruyko, and Nikos Komodakis. (2016). “Wide Residual Networks.” In: Proceedings of the British Machine Vision Conference 2016 (BMVC 2016).
- QUOTE: ... Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. ...