Transfer learning helps in reducing the computational cost of model training in several ways:

  1. Pretrained Models: Transfer learning leverages pretrained models that have been trained on large-scale datasets. These models have already learned basic features such as edges, textures, and shapes, which are common in many different types of images or texts. By using a pretrained model as a starting point, we can avoid the need to train a model from scratch, significantly reducing the computational cost. This is especially beneficial when working with limited computational resources or when training large models.

  2. Feature Extraction: Transfer learning allows us to extract useful features from a pretrained model without the need to recompute them. Instead of feeding raw data into the model and training all the layers, we can*****ze the pretrained layers and only train the additional layers added on top. This process only requires computing the gradients and updating the weights of the new layers, which is computationally cheaper compared to training all layers.

  3. Transfer of Knowledge: Transfer learning allows us to transfer knowledge from a source domain to a target domain. The knowledge learned from the source domain can be useful in guiding the learning process in the target domain, reducing the amount of data and computation needed to achieve good performance. By starting with a model that has already learned general patterns and concepts, we can fine-tune it on a smaller target dataset, which requires less computational resources compared to training a model from scratch.

  4. Data Augmentation: Transfer learning allows us to benefit from data augmentation techniques used during the training of the pretrained model. Data augmentation techniques involve applying various transformations (e.g., flipping, rotation, scaling) to the training data to increase its diversity and improve generalization. By reusing the augmented data generated during the original training, we can leverage the benefits of data augmentation without the need to compute the augmented data again, reducing the computational cost.

  5. Model Compression: Transfer learning can also enable model compression techniques. Pretrained models are usually large and computationally expensive. However, by using transfer learning, we can take advantage of the knowledge stored in the pretrained model and apply techniques like pruning, quantization, or low-rank approximation to compress the model and reduce its computational requirements while maintaining good performance.

  In summary, transfer learning reduces the computational cost of model training by leveraging pretrained models, reusing learned features, transferring knowledge, benefiting from data augmentation, and enabling model compression techniques. These strategies help overcome the computational limitations and improve training efficiency in various machine learning applications.