Name
Abstract | ||
|---|---|---|
In many learning tasks, including semantic image segmentation, performance can be effectively improved through the fine-tuning of a pre-trained convolutional network, instead of training from scratch. With fine-tuning, the underlying assumption is that the pre-trained model extracts generic features, which are at least partially relevant for solving a segmentation task, but that would be difficult to extract from the smaller amount of data that is available for training in urban driving scenes segmentation. However, besides the initialization with the pre-trained model and the early stopping, there is no mechanism in classical fine-tuning approaches for keeping the generic features. Even worse, the standard weight decay drives the parameters towards the origin and affects the learned features. In this paper, we show that a simple regularization that uses the pre-trained model as a reference consistently improves the performance when applied to semantic urban driving scene segmentation. Experiments are done on the Cityscapes dataset, with four different architectures of convolutional networks. |
Year | Venue | Field |
|---|---|---|
2018 | Intelligent Vehicles Symposium | Early stopping,Pattern recognition,Task analysis,Segmentation,Computer science,Feature extraction,Image segmentation,Artificial intelligence,Initialization,Recall,Semantics |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuhong Li | 1 | 28 | 7.56 |
| Franck Davoine | 2 | 313 | 32.67 |
| Yves Grandvalet | 3 | 995 | 93.81 |