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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Are Skip connections and residual connections(when used as backbone) in CNN same?

I have a certain level in understanding how the skip connection and residual connection works and I also got an intuition that they both are similar(Correct if am wrong). But here is my question. If i ...
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About memory savings in invertible neural networks (INNs)

I am trying to understand how much memory (in GBs) could be actually saved when switching to an INN. This paper proposed RevGAN for efficient image-to-image translation. The fundamental block here is ...
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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{...
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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 ...
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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 ...
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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 ...
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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? ...
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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 ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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 ...
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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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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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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 ...
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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 ...