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However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train.

More residual network variants try to improve performance by constructing deeper residual net- works, while the problem of diminishing feature ...

这个问题也被称之为diminishing feature reuse。当然在后续的工作中，很多人都朝着解决这个问题的方向做，比如residual block进行随机失活，类似于特殊的dropout。基于上述问题，作者认为widening of ResNet blocks可能会提供更有效的方法。

Diminishing feature reuse during forward propagation (also known as loss in ... between layers, which allow the network to pass on features ...

Jan 09, 2020 · Diminishing feature reuse [4] during forward propagation (also known as loss in information flow) refers to the analogous problem to vanishing gradients in the forward direction.

... and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train.

非常深的网络往往会出现diminishing feature reuse，这往往会导致网络的训练速度会变得相当的慢。 为了解决这个问题，本文提出了wide ResNet以 【深度学习】入门理解ResNet和他的小姨子们（四）---WideResNet_Shwan_ma的博客-程序员宝宝 - 程序员秘密

Diminishing Feature Reuse ... A Residual block with a identity mapping, which allows us to train very deep networks is a weakness. As the gradient ...

This brings several drawbacks: 1) training of very deep nets is affected by the diminishing feature reuse problem [23], where low-level features are washed ...

11.11.2017 · 非常深的网络往往会出现diminishing feature reuse，这往往会导致网络的训练速度会变得相当的慢。 为了解决这个问题，本文提出了wide ResNet以 【深度学习】入门理解ResNet和他的小姨子们（四）---WideResNet

Diminishing Feature Reuse. However, As gradient flows through the network there is nothing to force it to go through residual block weights ...

01.12.2018 · This problem was formulated as diminishing feature reuse. 2. WRNs (Wide Residual Networks) In WRNs, plenty of parameters are tested such as the design of the ResNet block, how deep (deepening factor l) and how wide (widening factor k) within the ResNet block. When k =1, it has the same width of ResNet. While k >1, it is k time wider than ResNet.

the problem of diminishing feature reuse for very deep residual networks makes these networks very slow to train. To conduct these problems, WRNs [39] generated residual networks by increasing ...

However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train.

Dec 01, 2018 · This problem was formulated as diminishing feature reuse. 2. WRNs (Wide Residual Networks) In WRNs, plenty of parameters are tested such as the design of the ResNet block, how deep (deepening factor l) and how wide (widening factor k) within the ResNet block. When k =1, it has the same width of ResNet. While k >1, it is k time wider than ResNet.

However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train.

这个问题在Highway network被称为diminishing feature reuse。 随机深度ResNet经过在训练时随机丢弃ResNet中的部分层来解决这个问题，这种方法能够视为dropout的特例，而该方法的有效性也证实了上述假设是正确的。

Stochastic Depth ResNet paper talked about features being "washed" away. ... the paragraph that's italic and says Diminishing Feature Reuse.

In very deep residual networks that should help deal with diminishing feature reuse problem enforcing learning in different residual blocks. 3 Experimental ...

Network depth is a major determinant of model expressiveness, both in theory [9, 10] and in practice [5, 7, 8]. However, very deep models also introduce new challenges: vanishing gradients in backward propagation, diminishing feature reuse in …

diminishing feature reuse (useful shallow features are lost in deep feedforward steps). These problems are more serious in the online setting (especially for the initial online perfor-mance), as we do not have the liberty to scan the data multiple times to overcome these issues (like we can in batch settings).

Mar 30, 2020 · In feature reuse, the meta-initialization already contains useful features that can be reused, so little adaptation on the parameters is required in the inner loop. To prove feature reuse is a competitive alternative to rapid learning in MAML, the authors proposed a simplified algorithm, ANIL, where the inner loop is removed for all but the ...

Diminishing feature reuse[4] during forward propagation (also known as loss in information flow) refers to the analogous problem to vanishing gradients in ...

diminishing feature reuse (useful shallow features are lost in deep feedforward steps). These problems are more serious in the online setting (especially for the initial online perfor-mance), as we do not have the liberty to scan the data multiple times to overcome these issues (like we can in batch settings).

Diminishing Feature Reuse: This is the same problem as the vanishing gradient, but in the forward direction. Features computed by early ...