Regularization in Computer Vision
In this chapter, we will explore another popular field of deep learning – computer vision. Computer vision is a large field with many tasks, from classification through generative models to object detection. Even though we can’t cover all of them, we will supply methods that can apply to all tasks.
In this chapter, we’ll cover the following recipes:
- Training a convolutional neural network (CNN)
- Regularizing a CNN with vanilla neural network (NN) methods
- Regularizing a CNN with transfer learning for object detection
- Semantic segmentation using transfer learning
At the end of this chapter, you will be able to handle several computer vision tasks such as image classification, object detection, instance segmentation, and semantic segmentation. You will be able to apply several tools to regularize the trained models, such as architecture, transfer learning, and freezing weights for fine-tuning.
