Questions tagged [residual-networks]
A type of neural network architecture suggested in 2015 that allows to train very deep networks with 100s or 1000s of layers.
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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 ...
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0answers
9 views
ResNet Reduce Block
I am building a ResNet. I have two separate blocks: Cnn block, Reduce block. Cnn block - 1 cnn layer, activation, Batch Normal -> 1 cnn, activation, Batch Normal, so 2 CNN in this block. In Reduce ...
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1answer
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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 ...
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Deep Learning for small 1-dim Datasets
I am trying to find a neural network architecture for a dataset (150 instances) with 10 features (numerical). The features are not associated to each other, so 1d-convolutions are not an option.
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1answer
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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 ...
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37 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 ...
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99 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 ...
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1answer
35 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 ...
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1answer
104 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, ...
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1answer
276 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 ...
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1answer
879 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 ...
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1answer
374 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 ...
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1answer
237 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 ...
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1answer
65 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 ...
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1answer
436 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 ...
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1answer
289 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 ...
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1answer
3k 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 ...
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1answer
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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 ...
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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, ...
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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
...
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0answers
112 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 ...
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1answer
301 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, ...
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1answer
478 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 ...
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1answer
9k views
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 ...
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2answers
3k views
Why residual networks works?
I have few questions, that apperad reading through paper:
Building block of residual network can be viewed as following: data passed to right branch -> convolution, scaling, convolution and in right ...
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2answers
395 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 ...
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2answers
1k 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 ...
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2answers
1k 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 ...
4
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1answer
835 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 ...
