Transfer learning in computer vision tasks: Remember where you come from
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Transfer learning in computer vision tasks : Remember where you come from. / Li, Xuhong; Grandvalet, Yves; Davoine, Franck; Cheng, Jingchun; Cui, Yin; Zhang, Hang; Belongie, Serge; Tsai, Yi Hsuan; Yang, Ming Hsuan.
In: Image and Vision Computing, Vol. 93, 103853, 2020.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Transfer learning in computer vision tasks
T2 - Remember where you come from
AU - Li, Xuhong
AU - Grandvalet, Yves
AU - Davoine, Franck
AU - Cheng, Jingchun
AU - Cui, Yin
AU - Zhang, Hang
AU - Belongie, Serge
AU - Tsai, Yi Hsuan
AU - Yang, Ming Hsuan
N1 - Funding Information: This work was carried out with the supports of the China Scholarship Council and of a PEPS grant through the DESSTOPT project jointly managed by the National Institute of Mathematical Sciences and their Interactions (INSMI) and the Institute of Information Science and their Interactions (INS2I) of the CNRS, France. It was carried out in the framework of SIVALab, a joint laboratory between Renault and Heudiasyc (UTC/CNRS). We acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research. Funding Information: This work was carried out with the supports of the China Scholarship Council and of a PEPS grant through the DESSTOPT project jointly managed by the National Institute of Mathematical Sciences and their Interactions (INSMI) and the Institute of Information Science and their Interactions (INS2I) of the CNRS, France. It was carried out in the framework of SIVALab, a joint laboratory between Renault and Heudiasyc (UTC/CNRS). We acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research. Publisher Copyright: © 2019 Elsevier B.V.
PY - 2020
Y1 - 2020
N2 - Fine-tuning pre-trained deep networks is a practical way of benefiting from the representation learned on a large database while having relatively few examples to train a model. This adjustment is nowadays routinely performed so as to benefit of the latest improvements of convolutional neural networks trained on large databases. Fine-tuning requires some form of regularization, which is typically implemented by weight decay that drives the network parameters towards zero. This choice conflicts with the motivation for fine-tuning, as starting from a pre-trained solution aims at taking advantage of the previously acquired knowledge. Hence, regularizers promoting an explicit inductive bias towards the pre-trained model have been recently proposed. This paper demonstrates the versatility of this type of regularizer across transfer learning scenarios. We replicated experiments on three state-of-the-art approaches in image classification, image segmentation, and video analysis to compare the relative merits of regularizers. These tests show systematic improvements compared to weight decay. Our experimental protocol put forward the versatility of a regularizer that is easy to implement and to operate that we eventually recommend as the new baseline for future approaches to transfer learning relying on fine-tuning.
AB - Fine-tuning pre-trained deep networks is a practical way of benefiting from the representation learned on a large database while having relatively few examples to train a model. This adjustment is nowadays routinely performed so as to benefit of the latest improvements of convolutional neural networks trained on large databases. Fine-tuning requires some form of regularization, which is typically implemented by weight decay that drives the network parameters towards zero. This choice conflicts with the motivation for fine-tuning, as starting from a pre-trained solution aims at taking advantage of the previously acquired knowledge. Hence, regularizers promoting an explicit inductive bias towards the pre-trained model have been recently proposed. This paper demonstrates the versatility of this type of regularizer across transfer learning scenarios. We replicated experiments on three state-of-the-art approaches in image classification, image segmentation, and video analysis to compare the relative merits of regularizers. These tests show systematic improvements compared to weight decay. Our experimental protocol put forward the versatility of a regularizer that is easy to implement and to operate that we eventually recommend as the new baseline for future approaches to transfer learning relying on fine-tuning.
KW - Computer vision
KW - Parameter regularization
KW - Transfer learning
U2 - 10.1016/j.imavis.2019.103853
DO - 10.1016/j.imavis.2019.103853
M3 - Journal article
AN - SCOPUS:85076296226
VL - 93
JO - Image and Vision Computing
JF - Image and Vision Computing
SN - 0262-8856
M1 - 103853
ER -
ID: 301823399