📌 Weight Freezing Summary
Weight freezing is a technique used in training neural networks where certain layers or parameters are kept unchanged during further training. This means that the values of these weights are not updated by the learning process. It is often used when reusing parts of a pre-trained model, helping to preserve learned features while allowing new parts of the model to adapt to a new task.
🙋🏻♂️ Explain Weight Freezing Simply
Imagine finishing a jigsaw puzzle and gluing some pieces together so they never move again. This lets you focus on changing only the loose pieces while keeping the important parts intact. In the same way, weight freezing lets a neural network keep some knowledge fixed while learning new things.
📅 How Can it be used?
You can freeze early layers of a pre-trained image recognition model and train only the final layers for a new classification task.
🗺️ Real World Examples
A company uses a pre-trained language model for customer service chatbots. They freeze most of the model’s weights to retain its general language skills, then train only the last few layers on their own support data to specialise the chatbot for their products.
A medical imaging team adopts a pre-trained neural network for X-ray analysis. They freeze the initial layers that detect basic shapes and patterns, then train just the later layers to recognise specific diseases relevant to their dataset.
✅ FAQ
What does weight freezing mean when training a neural network?
Weight freezing is when certain parts of a neural network are kept unchanged during training. This allows the model to hold on to useful knowledge it has already learned, while other parts can still be updated to learn new things.
Why would you want to freeze weights in a neural network?
Freezing weights is helpful if you are using a model that has already been trained on a big task, like recognising objects in images. By freezing some layers, you make sure the model does not forget important skills it has already picked up, even as it learns to handle a new task.
Can you still improve a model if some weights are frozen?
Yes, you can still improve the model. Freezing some weights means you keep valuable past knowledge, but you can continue to train and adapt the rest of the model to perform better on new data or tasks.
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