konversioonikihte

Konversioonikihte, often translated as conversion layers, are a concept used in machine learning, particularly in deep learning, to adapt a pre-trained model to a new, related task. This process is common when working with large datasets and complex model architectures. Instead of training a model from scratch for a new task, which can be computationally expensive and require a vast amount of new data, a pre-trained model serves as a strong starting point.

The core idea of a conversion layer is to modify the final layers of a pre-trained neural network. Typically, a model pre-trained on a large and general dataset, such as ImageNet for image classification, has learned to extract useful low-level and high-level features. The earlier layers of such a model capture these general features. For a new task, the final layers, which are usually responsible for making predictions specific to the original task, are removed or replaced. New layers, designed for the specific requirements of the new task, are then added. These new layers are often trained on the new, smaller dataset.

The weights of the earlier layers of the pre-trained model are usually kept frozen or are fine-tuned with a very small learning rate. This preserves the valuable feature extraction capabilities learned from the original, larger dataset. The newly added layers are then trained more extensively on the target dataset. This approach significantly speeds up training and often leads to better performance, especially when the target dataset is limited in size. Conversion layers are a form of transfer learning, a widely adopted technique in modern machine learning.