Humans are skilled in transferring our skills from one area to next. In event of brand challenge or task that is new in which we are not familiar we typically use our past experience to help us.
Similar to field of machine learning transfer learning is method which allows machines to use previously learned knowledge for new task.
Instead of starting over model is able to reuse what its learned before and can solve problems more efficiently and quickly. This is particularly beneficial in cases where data is insufficient for new challenge.
What is Transfer Learning?
Transfer learning can be described as method of learning in which model developed for one task can be used as basis for another job. This technique is advantageous when new task is closely related to previous one or if data needed to perform second task are not available.
By leveraging learned characteristics of first assignment model will be able to better adapt to task at hand which speeds up learning and improves performance. Transfer learning is also way to reduce risk of overfittingrisk because it already has universally applicable features for this second job.
Why is Transfer Learning Important?
Transfer learning is an essential technology in machine learning providing solutions to most pressing issues:
- Limited Data: In process of acquiring large quantities of information is usually difficult and can be costly. Transfer learning lets us make use of pre trained models and reduce dependence on massive data sets.
- Enhanced Performance: Starting with trained model that has already learned from extensive evidence HTML0 can provide quicker and more precise results for new tasks. It is ideal for those applications that require high precision as well as efficiency.
- Cost efficiency and time: Transfer learning shortens training times and helps conserve resources through use of already existing models and eliminating requirement to learn starting from scratch.
- Adaptability: Models trained on an individual task may be refined to perform related tasks which makes transfer learning adaptable to variety of application areas including image recognition and natural language processing.
How Does Transfer Learning Work?
Transfer learning is structured method to draw on existing knowledge using trained model to perform different tasks.
- A pre trained and trained model : Begin with an already trained model developed on vast database for task you want to. Pre trained models have learned basic features and patterns which are applicable to jobs.
- Basis Model It is pre trained standardized model called “the basic model” contains layers of data that are processed to understand hierarchical representations. that capture low level features to more complex.
- Transfer Layers Locate layers in base model which contain general information that can be used for both old and new task. layers which are usually at very top of network contain broad reuseable characteristics.
- The fine tuning : You can fine tune selected layers using information from task. This helps to retain previously learned skills while also adjusting parameters to satisfy particular demands of new job increasing accuracy and adaptability.
Features of low level acquired for Task could be useful as learning model for task B.
Frozen and Trainable Layers
When it comes to Transfer Learning two main elements help adapt models efficiently two main components are frozen layers and modifyable layers.
- Frozen Layers layers that are derived from model that was trained when fine tuning process. They preserve basic features that were that were learned during initial assignment which extracts universal patterns from input data.
- Modifiable Layers These layers can be adjusted when fine tuning to acquire particular features of data which allows model to be adapted to specific requirements for new task.
When to Use Transfer Learning
In same way as it is when it comes to field of machine learning It is difficult to establish general rules However here are some suggestions on how transfer learning can be applied:
- Insufficient training data: There isnt enough trained data that is labeled to build your system completely from ground up.
- Network in place: There already exists an existing network which has been trained in similar manner and is generally developed using massive quantities of information.
- The same input Task 1 and task 2 both have identical input.
If your original model was developed with an open source program such as TensorFlow You can rebuild it and then retrain few layers that are appropriate for task at hand. Remember that transfer learning can only be used in event that techniques acquired from previous task are general in nature meaning that they could be beneficial for other tasks that are related to it. Additionally input to model must be of same dimensions as what it was trained initially. If your input isnt as large include pre processing stage in order to adjust your inputs size according to required size.
Deciding which Layers to Freeze or Modify
The next question is what we can do to figure out layers that we should freeze and which ones must be trained. This is easy more youd like to take features that are inherited from an already trained model more need to freeze layers.
The degree of features you acquire of model that has been trained on size and asymmetry of dataset you are aiming to original dataset
Small and similar target dataset If data set is not large but its similar to baseline dataset fine tuning of only couple of layers can result in excessive fitting. In this scenario one or two layers that are fully connected are taken out and replaced by an additional layer that is compatible with classes of interest. remainder of model is frozen and only new layers are taught.
Large and similar target dataset If data set is big and similar to target likelihood of overfitting will be less. final fully connected layer is eliminated and replaced to correspond with newly created classes and whole model is refined to match latest dataset while preserving structure and adjusting to new size of data.
Small and different target Dataset If data set youre targeting is very small and unique high level functions of model originally used are not as useful. This is why you should remove all of top layers. and add layers in order to fit targeted classes and then train lower layers to accommodate particular data.
Large and different target Dataset for huge and distinct data sets model is most benefited by eliminating last layers as well as introducing layers that are specifically designed to meet requirements of job. whole model then gets modified without freezing layers in order in order to guarantee complete adaption.
Transfer learning can be highly efficient and efficient starting point typically leading to high quality outcomes and quicker adaptation to demands of new jobs.
Approaches to Transfer Learning
1. Training Model to Reuse it
Imagine youre looking to tackle task but you dont have enough data for training an advanced neural network. solution to this problem is to locate similar task that is brimming with of information. Develop deep neural network using task B. Then utilize model as basis to solve task. If youll require entire model or one or two layers is dependent upon task that youre trying to resolve.
If you are using identical inputs in each task reusing model while making predictions for new input could be viable option. Alternately changing or retraining various task specific layers as well as output layer can be an option to investigate.
2. Using Pre Trained Model
The other option is to make use of trained model. Theres variety of such models on market which is why you must take look. number of layers you can reuse and how many layers you can retrain will depend on particular problem.
Keras For instance it has number of models pre trained which can be utilized to learn transfer predict features extraction transfer learning as well as tuning. These models are available along with couple of short instructions on using these models on this page. There are variety of research organizations that offer model training programs.
Transfer learning is most frequently used during deep learning.
3. Feature Extraction
Another option is to employ deep learning to find most effective representation for problem. This means selecting best characteristics. This method is referred to as representation learning and often results in much higher efficiency that can be achieved with manually designed representations.
In machine learning features typically are created manually by domain experts and researchers. good news is that deep learning is able identify features in way that is automatic. However youll need to choose which elements you include in your network. But they are able to discern features that are essential and which arent. Representation learning algorithms can find suitable combination of functions in an extremely short period of time even in complex jobs that normally require lots of human energy.
The model learned can be utilized for different problems also. Use initial layers to identify proper model of features. However you shouldnt work with output of network as its specific to task at hand. Instead add data to your network using an intermediate layer to be an output layer. output layer is then considered as representation of data as it is.
This method is typically employed in computer vision as it reduces amount of data you have to store that reduces amount of computation required and makes it compatible with traditional algorithms too.
Applications of Transfer Learning
Transfer learning is widely utilized across range of fields which includes:
- Computer Vision: Transfer learning is common feature in tasks involving image recognition which are where models that have been trained on huge images are then adapted for particular tasks like facial recognition medical imaging as well as object detection.
- Natural Language Processing (NLP): In NLP models such as BERT GPT and ELMo are trained on huge corpora of text and then fine tuned to perform specific tasks such as sentiment analysis and machine translation and answering questions.
- Healthcare Transfer learning can help create diagnostic tools for medical use by leveraging knowledge of general model of image recognition to analyse medical images such as X rays and MRIs.
- Finance Transfer learning within finance can aid in detection of fraud in risk assessment fraud detection and credit scoring through transfer of patterns that have been learned from financial data.
Advantages of Transfer Learning
- Enhance training procedure:By using pre trained model model will improve its performance and speed in second step since it already has an understanding of characteristics and patterns of information.
- Improved performanceTransfer learning may result in better performance on second challenge because it can draw upon experience it learned from previous task.
- Small datasets to handle: When there is only small amount of data for next job transfer learning could assist in stopping overfitting because model has acquired general knowledge which are most likely to prove beneficial in next task.
Disadvantages of Transfer Learning
- Domain mismatchThe already trained model could not be suited for second job when two jobs are very different or if distribution of data between them differs greatly.
- Overfitting Transfer learning could cause overfitting when model is tuned excessively on second aspect since it might learn specific features for task which are not able to generalize for new information.
- Complexity model that is pre trained as well as fine tuning procedure is computationally costly and require special hardware.
Transfer learning is an extremely effective method in machine learning which improves models effectiveness by using knowledge of previous models that have been trained.
Beginning with models that are already in place and adjusting them to particular tasks transfers learning can save time enhances accuracy and allows for effective learning when data is limited.