Answers.Body: <p>Just by having more parameters, the deeper model has a <strong>higher capacity</strong> than the smaller one. This means that theoretically it can learn to extract more complex features from the data. Additionally, more layers means that the model can extract even <strong>higher-level features</strong> from the data.
  So, generally speaking, deeper models will most of the times <strong>outperform</strong> shallow ones for more difficult tasks.</p>
  
  <p>The downside is that if you have a small amount of data, a high-capacity model has the ability of memorizing the training set, which would lead to <strong>overfitting</strong>. 
  Besides performance, deeper models require better hardware and larger training times. So, there are plenty of reasons for one to prefer a more shallow model to a deeper one.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-03-16T22:03:46.120Z
Answers.LastActivityDate: 2020-03-16T22:03:46.120Z
Answers.UserDisplayName: Djib2011
Body: <p>If you have an <span class="math-container">$18$</span> layer residual network versus and a <span class="math-container">$32$</span> layer residual network, why would the former do better at object detection than the latter, if you have both models are training using the same training data?</p>
  
Tags: <convolutional-neural-networks><object-detection><residual-networks>
Title: Do deeper residual networks perform better or worse?
UserDisplayName: Stockguu
ThreadText: # Do deeper residual networks perform better or worse?
  
  (Asked by: username_0 on 2020-03-16)
  
  If you have an $18$ layer residual network versus and a $32$ layer residual network, why would the former do better at object detection than the latter, if you have both models are training using the same training data?
  
  **Tags:** convolutional-neural-networks, object-detection, residual-networks
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-03-16. Score: 1
  
  Just by having more parameters, the deeper model has a **higher capacity** than the smaller one. This means that theoretically it can learn to extract more complex features from the data. Additionally, more layers means that the model can extract even **higher-level features** from the data.
  So, generally speaking, deeper models will most of the times **outperform** shallow ones for more difficult tasks.
  
  The downside is that if you have a small amount of data, a high-capacity model has the ability of memorizing the training set, which would lead to **overfitting**.
  Besides performance, deeper models require better hardware and larger training times. So, there are plenty of reasons for one to prefer a more shallow model to a deeper one.
  
  
  ---
  
  