What is transfer learning in computer vision?
An example of an AI chatbot conversation about transfer learning in computer vision.
What is transfer learning in computer vision?
Transfer learning in computer vision is a technique where a model developed for a specific task is reused as the starting point for a model on a second task.
How does it improve model performance?
It improves performance by leveraging knowledge gained from a large dataset, allowing the model to generalize better on smaller datasets.
Can you give an example of its application?
Sure! A common application is using a pre-trained model like VGG16 for image classification tasks, which can be fine-tuned for specific categories.
What are the benefits of using transfer learning?
The benefits include reduced training time, improved accuracy, and the ability to work with limited data.
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