Did you know that AI's carbon footprint could be significantly reduced? The key lies in a technique called 'transfer learning' or 'fine-tuning', where previously trained models' weights are leveraged as a starting point to train new models. This approach, also known as 'frozen weights', has been shown to save up to 90% of computation time and energy required for model training.
By utilizing pre-trained models, we can skip the time-consuming and energy-intensive process of training models from scratch. This is especially useful for tasks with limited training data, where the benefits of pre-trained models are most pronounced. For instance, if we want to train a model to recognize objects in images, we can use a pre-trained model like VGG16 or ResNet50 as a starting point and fine-tune its weights for our specific task.
The benefits of frozen weights extend beyond energy efficiency. By leveraging pre-trained models, we can also reduce the risk of overfitting, as the pre-trained mode...
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