Considering the lengthening timelines for deep machine learning and AI projects to fight against COVID-19 the interest in transfer learning has grown significantly. Transfer learning for deep machine learning is the process of first training a base network on a benchmark dataset (like ImageNet), and then transferring the best-learned network features (the network’s weights and structures) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network’s generalization capabilities significantly in many application areas.
Transfer learning is currently used in almost every deep learning model when the target dataset does not contain enough labeled data. Building deep learning models from scratch and training with huge data is very expensive, both in time and resources. Transfer learning is very effective for rapid prototyping, resource efficiency and high performance. As human brain carry forward knowledge and wisdom and learn it from others, transfer learning mimic this type behavior.
Transfer Learning Base Models
To design an efficient neural network model, you need to know the details of different base models. Because from the base model you will be transferring the knowledge to your new model. Here, knowledge means the network structures and the weights.… Read more..