Newest 'residual-networks' Questions

2

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Is it necessary to skip two or more layers in ResNets?

I was watching Andrew Ng's video about ResNets and in that video he skipped 2 layers while "short cutting" them. For example, if we have input $a^{l}$, next layer will be $a^{l+1} = g(z^{l+1}...

neural-networks residual-networks

asked Jan 8 at 13:35

dosvarog

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votes

1answer

74 views

Are there any weight matrices of residual connections in ResNet?

In the resnet and its variants, such as (taken from here) Do these shortcut connections have any weight matrices (and bias) associated with them or do they simply copy the same output and transfer it ...

asked Dec 23 '21 at 10:50

Mas A

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equal error rate increasing while training speaker recognition model

I'm training a ResNet-based speaker recognition model. This is a fixed-data training protocol. I am validating my model on a different test set. The issue is the more I am training it seems the VEER ...

neural-networks speech-recognition residual-networks

asked Oct 21 '21 at 6:28

Zabir Al Nazi

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Look ahead bias in the skip connection of the Transformer-decoder/GPT2 architecture

How come the residual connection on the attention module in Decoder-Transformers/GPT2 does not cause a look ahead bias? This is my current understanding: GPT is similar to the decoder side of the ...

transformers residual-networks

asked Oct 16 '21 at 10:20

Alessandro

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6 views

How does Residual Shift work in the Temporal Shift Paper?

While reading this paper, I came across the paragraph, "Instead of inserting it in-place, we put the TSM inside the residual branch in a residual block. We denote such version of shift as ...

conv-neural-network residual-networks

asked Sep 3 '21 at 2:34

desert_ranger

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vote

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Why are shrinkage methods not effective for regularizing CNN?

This is going to be a very specific question. TLDR: I tried to use various shrinkage methods (L1, L2,...) to "emulate" what ResNet does, but they all sucked. Why? I'd like to start with a ...

neural-networks conv-neural-network regularization residual-networks

asked Jun 12 '21 at 0:27

Bayequentist

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64 views

Max Pooling vs Average Pooling for residual/skip connections

I've implemented a CNN with skip connections; some connections skip across residual blocks with no spatial downsampling but some connections skip across blocks that have convolutions with a stride of ...

machine-learning neural-networks conv-neural-network residual-networks

asked Jun 9 '21 at 17:48

Avelina

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2

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27 views

Why is the block size in ResNets usually 2 or 3 layers and not more?

Is there a reason why only 2 or 3 layers are skipped by a shortcut connection? What benefits would it have to skip more than 3 layers? I do not find an obvious answer besides that increasing the block ...

machine-learning neural-networks references residual-networks

asked Jun 4 '21 at 10:34

alexander_

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3

votes

1answer

202 views

Is a Residual Network a Feedforward Network?

I am really confused about people comparing feedforward networks to residual networks. This is done in several papers I have read into (just one for example, first line in this paper: https://arxiv....

neural-networks residual-networks

asked May 19 '21 at 10:18

user84037

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1

vote

0answers

121 views

Does the 'skip' in a residual network actually happen in the backward pass?

The following is a question regarding a residual block. Let $A^{[l-1]\{t\}}$ denote the activation of the $(l-1)^\mathrm{th}$ layer of a particular fully connected neural network, given the $t^\mathrm{...

neural-networks backpropagation residual-networks

asked Jun 13 '20 at 21:11

joshuaheckroodt

1234 bronze badges

2

votes

1answer

14 views

Residual Networks - Non-image use cases

I've been doing some research into computer vision lately and constantly come across the ability of residual networks to improve performance. Intuitively I think I grasp them, however, I struggle to ...

neural-networks residual-networks

asked May 25 '20 at 13:53

Adam Green

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1

vote

0answers

108 views

How to calculate number-of-ops used in the backward pass of the neural net in training phase?

I'm trying to study a basic model like ResNet and how many operations it does and memory usage during backward-pass. For forward pass for layer like 1x1 conv or 3x3 conv, i was able to easily compute ...

neural-networks modeling conv-neural-network gradient-descent residual-networks

asked Apr 29 '20 at 19:04

Joe Black

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-1

votes

1answer

102 views

Where is BatchNorm performed in ResNeXT https://github.com/facebookresearch/ResNeXt neural network?

In the original paper that described ResNeXT (variation of Resnet) at https://arxiv.org/pdf/1611.05431.pdf. On Page-5 top right column, it says: ReLU is performed right after eachBN, expect for ...

neural-networks residuals conv-neural-network batch-normalization residual-networks

asked Apr 14 '20 at 1:21

Joe Black

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vote

1answer

439 views

how is this Bottleneck design the same as original residual block in resnet?

This paper/link talks about resnet's bottleneck design. It's totally not clear to me how the bottleneck design on the right is equivalent to the left-diagram and how is it reducing the parameters? ...

neural-networks conv-neural-network residual-networks

asked Apr 7 '20 at 0:10

Joe Black

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3

votes

1answer

1k views

Why does residual block in resnet shown as skipping not just 1-layer (conv + relu) but also the next weight layer?

I was reading about resnet at this link. This link and others say that residual block skips 1-layer, but then all of them show a diagram where there is an additional weight layer (i think it can be ...

neural-networks conv-neural-network residual-networks

asked Apr 6 '20 at 23:09

Joe Black

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vote

0answers

121 views

How to pad skip connections when using transposed / deconvolutional layers

if you have a standard CNN architecture with convolutional layers there are 2 reasons why the identity of the skip connection can't be added with the current output. 1) There was pooling between the ...

neural-networks conv-neural-network residual-networks transposed-convolution

asked Nov 22 '19 at 14:27

Peter111

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1answer

91 views

Can residual connection affect the learning of skipped layers?

My understanding of residual connection is it could help avoid degradation by making $F(x)$, the skipped layers, close to 0 so that when $F(x)$ become redundant it won't hurt the performance. What ...

neural-networks residual-networks

asked Nov 1 '19 at 10:55

eric2323223

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1answer

22 views

How do Residual layers not inhibit the learning?

My understanding of residual layers is that we can take a layer and branch it into two paths. One going the typical path for a network, and the other is an identity mapping forward over the ...

residual-networks

asked Aug 3 '19 at 0:31

Matthew Kerian

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3

votes

0answers

84 views

How harmful is a wide Dense layer after a narrow?

My CNN-LSTM EEG Keras classification model includes a Dense 'shortcut' connection for residual sequence learning as shown below; to match dimensionality, the Dense layer's set to ...

neural-networks lstm information-theory autoencoders residual-networks

asked Jul 13 '19 at 18:08

OverLordGoldDragon

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vote

0answers

239 views

Why is my keras resnet50 model overfitting? [duplicate]

I have applied Keras ResNet-50 on a small x-ray image dataset. I tried making layers both trainable and non-trainable, but my model validation accuracy doesn't improve above 50%. I don't understand ...

neural-networks overfitting image-processing keras residual-networks

asked Jun 13 '19 at 18:58

Mobassir Hossen

1217 bronze badges

1

vote

1answer

174 views

Why resolution is not important for pre-trained models

As far as I understand (and even successfully applied in Kaggle competition), it's possible to feed images of any resolution into the pre-trained model (e.g. ResNet34). But I do not understand, why it ...

deep-learning convolution pre-training residual-networks

asked Jan 19 '19 at 14:25

Bi0max

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votes

1answer

308 views

Residual Network: Is small deviation of layer response good? What's the point of resnet?

I have 2 questions... In the paper Deep Residual Learning for Image Recognition, it says We show by experiments (Fig. 7) that the learned residual functions in general have small responses, ...

neural-networks residual-networks

asked Jan 15 '19 at 12:07

user3681105

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1

vote

1answer

463 views

ResNet 34 training with custom dataset

I am a beginner in Neural Networks and wanted to implement ResNet34 for a pet project at my workplace. Due to confidentiality issues, I do not want to use ImageNet trained weights. I have a dataset ...

machine-learning neural-networks residuals residual-networks

asked Dec 13 '18 at 21:22

Harsh Rawat

1112 bronze badges

1

vote

1answer

3k views

How does backpropagation in residual network works?

i am studying the deep residual network currently, and i cannot fully understand how backpropagation in residual network works. Here some parts of the paper that i read. so, how does the ...

machine-learning deep-learning residual-networks

asked Oct 9 '18 at 1:59

Mario Kristanto

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2

votes

1answer

855 views

ResNet: What is the content of the second skip-connection?

I have a question regarding the second skip connection in ResNet. Here is a part of the image of the architecture as it was presented in the paper: As I understand the output of the pool layer gets ...

neural-networks conv-neural-network residual-networks

asked Aug 3 '18 at 15:38

Peter111

1584 bronze badges

5

votes

1answer

410 views

Can a residual neural network be interpreted as a form of ensemble learning?

I stumbled over this paper: "Veit, A., Wilber, M. & Belongie, S. Residual Networks Behave Like Ensembles of Relatively Shallow Networks. (2016).". In it, they argue that residual neural ...

neural-networks conv-neural-network residual-networks

asked Jun 15 '18 at 13:12

Julian Carpenter

1735 bronze badges

3

votes

1answer

440 views

How to initialize weights in the presence of skip connections?

Weight initialization is an important parameter for success in large networks, in the absence of techniques such as batch normalization that reduces their impact. There are known initialization ...

neural-networks residual-networks

asked May 22 '18 at 5:07

P-Gn

1377 bronze badges

3

votes

1answer

1k views

How do the residual blocks prevent exploding gradients?

I am reading Roger Grosse's lecture notes on ResNet and I have a question regarding the explanation on how residue blocks prevent gradient explosion, see the screenshot below: My confusion is: this ...

machine-learning gradient residual-networks

asked May 14 '18 at 14:33

Y. Pei

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votes

1answer

431 views

Residual connection in Xception [closed]

I am trying to understand residual connection in Xception. If I am getting right, there's nothing really happen in residual connection (right figure) because it is just addition. But, I could not ...

deep-learning residual-networks

asked Apr 15 '18 at 23:29

Troy

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2

votes

1answer

5k views

How resNet increasing the dimension?

In the above image, It is the part of the resNet Architecture, here they have used dotted line to increase the dimension, but my question is How they are increasing the dimension?? or this dotted line ...

machine-learning deep-learning conv-neural-network residual-networks

asked Feb 23 '18 at 17:01

Sudip Das

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26

votes

4answers

34k views

What are "residual connections" in RNNs?

In Google's paper Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, it is stated Our LSTM RNNs have $8$ layers, with residual connections between ...

neural-networks lstm rnn residual-networks machine-translation

asked Jan 1 '18 at 3:03

user82135

2

votes

1answer

1k views

Residual network: why is each block learning residual error with respect to identity mapping?

In original version of neural network, it learns H(x) from input x, and in the residual network, it is said that learning is improved by learning only residual error with respect to identity mapping, ...

neural-networks deep-learning residual-networks

asked Oct 26 '17 at 14:23

username123

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11

votes

3answers

8k views

Shortcut connections in ResNet with different spatial sizes

If I take Fig.3 of the paper "Deep residual learning for image recognition", and look at the following piece of the residual network: $3\times3$ conv, 64 filters ...

deep-learning tensorflow residual-networks

asked Mar 1 '17 at 22:01

floflo29

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vote

0answers

134 views

Validation error smaller than training error in Deep Residual Learning for Image Recognition paper

At one of the examples of Deep Residual Learning for Image Recognition paper, figure 4, leftmost graph it's said that thin curves denote training error and bold curves validation error. Throughout ...

regression machine-learning cross-validation deep-learning residual-networks

asked Feb 15 '17 at 13:07

maz

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0

votes

1answer

378 views

How neural networks manages to learn 0 class for sigmoidal Cross-Entropy loss function using ReLU as activation unit?

Let's say we have binary classification problem: 0 vs 1. Some set of images needs to be mapped to those labels by convolutional neural network. Main trend for now is to use ReLu as activation unit, ...

neural-networks conv-neural-network residual-networks

asked Jan 19 '17 at 15:05

Il'ya Zhenin

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4

votes

1answer

640 views

Are shortcut connections with stride > 1 still "identity mappings" in ResNets?

In Deep Residual Learning for Image Recognition, I am trying to understand better the "dotted shortcuts" from Figure 3, where the first convolutional layer in those shortcuts is applied with stride of ...

conv-neural-network residual-networks

asked Dec 16 '16 at 21:48

JefferyRPrice

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11

votes

1answer

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What exactly is a Residual Learning block in the context of Deep Residual Networks in Deep Learning?

I was reading the paper Deep Residual Learning for Image Recognition and I had difficulties understanding with 100% certainty what a residual block entails computationally. Reading their paper they ...

machine-learning neural-networks deep-learning conv-neural-network residual-networks

asked Nov 20 '16 at 4:51

Charlie Parker

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9

votes

3answers

6k views

Why do residual networks work?

I have a few questions about the paper Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. The building blocks of residual networks can be viewed as ...

machine-learning neural-networks conv-neural-network residual-networks

asked Sep 27 '16 at 12:37

Il'ya Zhenin

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3

votes

2answers

423 views

Do ResNets from Microsoft experts represent convolutional neural nets?

I am talking about the article of Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun (Microsoft Research team): "Deep Residual Learning for Image Recognitione (2015)" ResNets won the 1-st place at ...

machine-learning neural-networks deep-learning conv-neural-network residual-networks

asked Sep 17 '16 at 10:14

Andrey Radoncev

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5

votes

2answers

2k views

How does the Identity connection in ResNets work

I am currently going through the Research paper "Deep Learning for Image recognition" by Kaiming He. I don't quite understand the concept of shortcut connections. Suppose the input to the residual ...

deep-learning conv-neural-network residual-networks

asked Jun 29 '16 at 19:10

Newstein

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13

votes

2answers

2k views

Are Residual Networks related to Gradient Boosting?

Recently, we saw the emergence of the Residual Neural Net, wherein, each layer consists of a computational module $c_i$ and a shortcut connection that preserves the input to the layer such as the ...

machine-learning neural-networks deep-learning gradient-descent residual-networks

asked May 24 '16 at 6:05

rhadar

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6

votes

1answer

1k views

Residual network dimension changing blocks identity function

In trying to implement ResNet with bottleneck blocks for myself, I got very confused about the identity function residual blocks with different dimensions. They compared identity, conv projections on ...

machine-learning deep-learning residuals conv-neural-network residual-networks

asked Apr 12 '16 at 6:16

Pioter

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