Pre-trained model
- See also: Machine learning terms
Introduction
In the field of machine learning, a pre-trained model refers to a model that has been previously trained on a large dataset and can be fine-tuned for a specific task. The motivation behind using a pre-trained model is to leverage the knowledge gained during its initial training, thus reducing the time, computational resources, and the amount of data required for training a new model from scratch.
Pre-training Methods
There are various pre-training methods employed in machine learning, such as unsupervised, self-supervised, and supervised pre-training.
Unsupervised Pre-training
In unsupervised pre-training, the model learns to generate representations of the input data without using any labels. This is typically achieved by training the model to reconstruct the input data from a lower-dimensional representation. Examples of unsupervised pre-training methods include autoencoders and clustering algorithms.
Self-supervised Pre-training
Self-supervised pre-training is similar to unsupervised pre-training, but it leverages the structure within the data to create proxy tasks with automatically generated labels. In this approach, the model learns to generate useful representations by solving these proxy tasks. For example, in natural language processing, models like BERT and GPT are pre-trained using self-supervised techniques such as masked language modeling and next-sentence prediction.
Supervised Pre-training
Supervised pre-training involves training the model on a large labeled dataset before fine-tuning it for a specific task. This approach is commonly used in transfer learning, where the knowledge learned from one domain or task is applied to another. Examples of supervised pre-trained models include ImageNet pre-trained convolutional neural networks (CNNs) used for image recognition tasks.
Benefits of Pre-trained Models
The use of pre-trained models offers several advantages, including:
- Improved performance: Pre-trained models often achieve better performance compared to models trained from scratch, as they have already learned useful features and representations from their initial training.
- Reduced training time: Fine-tuning a pre-trained model requires less time compared to training a new model from scratch, as the initial weights and architectures have already been optimized.
- Data efficiency: Pre-trained models can be fine-tuned with relatively smaller datasets, making them useful for tasks with limited labeled data.
Explain Like I'm 5 (ELI5)
A pre-trained model is like a well-trained chef who knows how to cook many dishes. When you need them to cook a new dish, they can use their existing knowledge and skills to prepare it faster and better than someone who has never cooked before. In machine learning, a pre-trained model has already learned from lots of data, so it can help solve new problems more quickly and efficiently.