Conventionally, data mining algorithms and machine learning algorithms are engineered to advance the problem in isolation. These are the algorithms engaged to train the model in separation on a specific feature space and the distribution process.
Depending upon the organization cases, models are trained by applying a machine learning algorithm for a definite task. A widespread inference in the field of machine learning is that training and test data must have distinctive feature spaces with the underlying distribution.
Even in this sphere the assumption may not hold and need to be rebuilt from the scratch according to the features and the distributive changes.
Transferring of knowledge or transfer learning from disparate domains made it desirable by reusing a pre-trained model knowledge for other tasks which can be further used for classification, regression, and clustering problems.
As humans have underlying skill in order to transfer the knowledge across different activities which is acquired by performing the specific tasks to iron out the related activities.
The transfer is a cognitive practice whereby a learner’s mastery of knowledge or skills in a particular context is enabled by applying the knowledge in a decisive context. Machine learning and deep learning algorithms have been traditionally designed to work for the significant feature-space distribution.
Once the feature-space distribution differs so that specific models need to be redesigned from scratch, which cumbersome task to gather the required training data.
Consequently, deep learning models during training need enough labeled data wherein it is impossible to create machine learning-based models for a target domain that consists of very few labeled data for supervised learning.
The fundamental motivation for transfer learning in the field of machine learning which focuses on the need for lifelong learning methods to retain and reuse the previously learned knowledge.
A learning technique to transfer learning is the multi-task learning framework, which tries to learn numerous tasks simultaneously even when they are different. A symbolic approach for multi-task learning is to uncover the common (latent) features that can avail each task.
Convolutional Neural Network is a type of artificial neural network that uses multiple perceptron’s which analyzes image inputs and have learnable weights based on the several parts of images by enabling it to segregate each other.
One advantage of using a Convolutional Neural Network is it leverages the use of local spatial coherence in the input images, which allows them to have fewer weights as some of the parameters shared. This process is clearly effective in terms of memory and intricacy.
ImageNet has developed a large database of images with annotations such as images and their labels using the pre-trained models like InceptionV1, Inception V2, VGG-16, and VGG-19 are already trained on ImageNet which encompasses disparate categories of images.
These models are built from scratch and trained by using a high GPU over millions of images dwells thousands of image categories.
As the model is trained on the huge dataset, it has learned a good personification of low-level features like spatial, edges, rotation, lighting, shapes wherein these features can be shared in order to facilitate the knowledge transfer and act as a feature extractor for new images in different computer vision problems.
Though these new images are difficult to categorize, the pre-trained models still are able to extract relevant features from these images based on the principles of transfer learning.
The power of transfer learning is unleashed by using the pre-trained model VGG-16 as a feature extractor to classify with the fewer training images.