A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions
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arXiv
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| Format: | Preprint |
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2023
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| author | Pihlgren, Gustav Grund Nikolaidou, Konstantina Chhipa, Prakash Chandra Abid, Nosheen Saini, Rajkumar Sandin, Fredrik Liwicki, Marcus |
| author_facet | Pihlgren, Gustav Grund Nikolaidou, Konstantina Chhipa, Prakash Chandra Abid, Nosheen Saini, Rajkumar Sandin, Fredrik Liwicki, Marcus |
| contents | In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation, and autoencoding. Deep perceptual loss is a type of loss function for images that computes the error between two images as the distance between deep features extracted from a neural network. Most applications of the loss use pretrained networks called loss networks for deep feature extraction. However, despite increasingly widespread use, the effects of loss network implementation on the trained models have not been studied.
This work rectifies this through a systematic evaluation of the effect of different pretrained loss networks on four different application areas. Specifically, the work evaluates 14 different pretrained architectures with four different feature extraction layers. The evaluation reveals that VGG networks without batch normalization have the best performance and that the choice of feature extraction layer is at least as important as the choice of architecture. The analysis also reveals that deep perceptual loss does not adhere to the transfer learning conventions that better ImageNet accuracy implies better downstream performance and that feature extraction from the later layers provides better performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_04032 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions Pihlgren, Gustav Grund Nikolaidou, Konstantina Chhipa, Prakash Chandra Abid, Nosheen Saini, Rajkumar Sandin, Fredrik Liwicki, Marcus Computer Vision and Pattern Recognition Machine Learning In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation, and autoencoding. Deep perceptual loss is a type of loss function for images that computes the error between two images as the distance between deep features extracted from a neural network. Most applications of the loss use pretrained networks called loss networks for deep feature extraction. However, despite increasingly widespread use, the effects of loss network implementation on the trained models have not been studied. This work rectifies this through a systematic evaluation of the effect of different pretrained loss networks on four different application areas. Specifically, the work evaluates 14 different pretrained architectures with four different feature extraction layers. The evaluation reveals that VGG networks without batch normalization have the best performance and that the choice of feature extraction layer is at least as important as the choice of architecture. The analysis also reveals that deep perceptual loss does not adhere to the transfer learning conventions that better ImageNet accuracy implies better downstream performance and that feature extraction from the later layers provides better performance. |
| title | A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2302.04032 |