Why Use Transfer Learning?

What is Transfer Learning?

Transfer learning is a technique in deep learning where a pre-trained model on a large dataset is reused as a starting point for a new task. This approach significantly reduces training time and improves performance, especially when dealing with limited datasets....

Important Concepts of Transfer Learning

Pre-trained models: These are deep learning models that have been pre-trained on the large datasets such as ImageNet for vision tasks, and these pre-trained models can be used by developers in transfer learning.Fine-tuning: With this technique, the pre-trained model will still be used as a basis for re-training with a new dataset that employs a very small learning rate just to be able to transfer it to a new task.Feature extraction: The second approach is referred to as the fine-tuning approach, which involves using the pre-trained model as a fixed feature extractor; here only the final classification layer is replaced during training.Normalize(mean=[0.5], std=[0.5]): Normalize the tensor by mean subtraction and standard devision. This is because it normalizes the input data by subtracting the mean and dividing it by the standard deviation which will be able to increase the speed of model training.Transformer: Transforms are a key data preprocessing stage in computer vision tasks, thus allow transforming the input data to the suitable form and scale so that the model can be processed efficiently. These Transform operations prove to be effective for the imaging of computer vision tasks, especially for deep learning models training and testing on image datasets....

Why Use Transfer Learning?

There are several compelling reasons to use transfer learning in machine learning, especially for deep learning tasks like image recognition or natural language processing:...

Pytorch for Transfer Learning

With PyTorch, the developers have an open source machine learning library for Python therein we experience the computational graph-based and dynamic approach that is flexible for building and training Neural Networks. It has the following features:...

Steps to Implement Transfer Learning for Image Classification in PyTorch

Transfer learning for image classification is essentially reusing a pre-trained neural network to improve the result on a different dataset. Follow the steps to implement Transfer Learning for Image Classification....

Transfer Learning in PyTorch : Implementation

ResNet 50 Implementation...

Result

After exploring 2 types of Transfer Learning we see the following results:...