Transfer Learning vs. Deep Learning: A Comprehensive Overview
In the rapidly evolving field of artificial intelligence, both transfer learning and deep learning stand out as powerful techniques. While deep learning provides the architecture and algorithms for complex learning tasks, transfer learning offers a way to accelerate and improve the training process by leveraging knowledge gained from previous tasks. This article explores the nuances of both concepts, their differences, applications, and how they are reshaping the landscape of machine learning.
Introduction to Transfer Learning
Transfer learning is a machine learning method where a pre-trained model is reused as the starting point for a model on a new task. Instead of training a model from scratch, transfer learning leverages the knowledge (features, weights, etc.) acquired from a pre-trained model to proceed with a new, related task. This approach is an optimization that allows for rapid progress when modeling the second task.
The core idea behind transfer learning is that a model trained on one task can be repurposed on a second, related task. By applying transfer learning to a new task, one can achieve significantly higher performance than training with only a small amount of data. Transfer learning is so common that it is rare to train a model for image or natural language processing-related tasks from scratch. Researchers and data scientists prefer to start from a pre-trained model that already knows how to classify objects and has learned general features like edges and shapes in images.
Traditional Machine Learning vs. Transfer Learning
Deep learning experts introduced transfer learning to overcome the limitations of traditional machine learning models. Here's a comparison:
Computational Efficiency: Traditional machine learning models require training from scratch, which is computationally expensive and requires a large amount of data to achieve high performance. Transfer learning, on the other hand, is computationally efficient and helps achieve better results using a smaller dataset.
Read also: University of Florida Transfer
Isolated vs. Knowledge-Based Training: Traditional machine learning has an isolated training approach where each model is independently trained for a specific purpose, without any dependency on past knowledge. Transfer learning uses knowledge acquired from the pre-trained model to proceed with the task. For instance, one cannot use the pre-trained model of ImageNet with biomedical images because ImageNet does not contain images belonging to the biomedical field.
Performance Speed: Transfer learning models achieve optimal performance faster than traditional machine learning models. The models that leverage knowledge (features, weights, etc.) from previously trained models already understand the features, making it faster than training neural networks from scratch.
Classical Transfer Learning Strategies
Different transfer learning strategies and techniques are applied based on the domain of the application, the task at hand, and the availability of data. Before deciding on the strategy of transfer learning, it is crucial to answer the following questions:
Which part of the knowledge can be transferred from the source to the target to improve the performance of the target task?
When to transfer and when not to, so that one improves the target task performance/results and does not degrade them?
Read also: GPA for Transfer Students
How to transfer the knowledge gained from the source model based on our current domain/task?
Traditionally, transfer learning strategies fall under three major categories depending upon the task domain and the amount of labeled/unlabeled data present:
1. Inductive Transfer Learning
Inductive Transfer Learning requires the source and target domains to be the same, though the specific tasks the model is working on are different. The algorithms try to use the knowledge from the source model and apply it to improve the target task. The pre-trained model already has expertise on the features of the domain and is at a better starting point than if we were to train it from scratch.
Inductive transfer learning is further divided into two subcategories depending upon whether the source domain contains labeled data or not. These include multi-task learning and self-taught learning, respectively.
2. Transductive Transfer Learning
Scenarios where the domains of the source and target tasks are not exactly the same but interrelated use the Transductive Transfer Learning strategy. One can derive similarities between the source and target tasks. These scenarios usually have a lot of labeled data in the source domain, while the target domain has only unlabeled data.
Read also: Bruin Day for Transfer Students
3. Unsupervised Transfer Learning
Unsupervised Transfer Learning is similar to Inductive Transfer learning. The only difference is that the algorithms focus on unsupervised tasks and involve unlabeled datasets both in the source and target tasks.
Common Approaches to Transfer Learning
Another way of categorizing transfer learning strategies is based on the similarity of the domain and independent of the type of data samples present for training.
1. Homogeneous Transfer Learning
Homogeneous Transfer learning approaches are developed and proposed to handle situations where the domains are of the same feature space. In Homogeneous Transfer learning, domains have only a slight difference in marginal distributions. These approaches adapt the domains by correcting the sample selection bias or covariate shift.
Instance Transfer: This covers a simple scenario in which there is a large amount of labeled data in the source domain and a limited number in the target domain. Both the domains and feature spaces differ only in marginal distributions. Instance-based Transfer learning reassigns weights to the source domain instances in the loss function.
Parameter Transfer: The parameter-based transfer learning approaches transfer the knowledge at the model/parameter level. This approach involves transferring knowledge through the shared parameters of the source and target domain learner models. In general, there are two ways to share the weights in deep learning models: soft weight sharing and hard weight sharing.
Feature-Representation Transfer: Feature-based approaches transform the original features to create a new feature representation. This approach can further be divided into two subcategories, i.e., asymmetric and symmetric Feature-based Transfer Learning.
Relational-Knowledge Transfer: Relational-based transfer learning approaches mainly focus on learning the relations between the source and a target domain and using this knowledge to derive past knowledge and use it in the current context.
2. Heterogeneous Transfer Learning
Transfer learning involves deriving representations from a previous network to extract meaningful features from new samples for an inter-related task. However, these approaches forget to account for the difference in the feature spaces between the source and target domains.
Heterogeneous Transfer Learning methods are developed to address such limitations. This technique aims to solve the issue of source and target domains having differing feature spaces and other concerns like differing data distributions and label spaces. Heterogeneous Transfer Learning is applied in cross-domain tasks such as cross-language text categorization, text-to-image classification, and many others.
Transfer Learning for Deep Learning
Domains like natural language processing and image recognition are considered to be the hot areas of research for transfer learning. There are also many models that achieved state-of-the-art performance.
These pre-trained neural networks/models form the basis of transfer learning in the context of deep learning and are referred to as deep transfer learning. Deep learning systems are layered architectures that learn different features at different layers. Initial layers compile higher-level features that narrow down to fine-grained features as we go deeper into the network.
These layers are finally connected to the last layer (usually a fully connected layer, in the case of supervised learning) to get the final output. This opens the scope of using popular pre-trained networks (such as Oxford VGG Model, Google Inception Model, Microsoft ResNet Model) without its final layer as a fixed feature extractor for other tasks.
The key idea here is to leverage the pre-trained model's weighted layers to extract features, but not update the model's weights during training with new data for the new task. The pre-trained models are trained on a large and general enough dataset and will effectively serve as a generic model of the visual world.
Fine-Tuning Off-the-Shelf Pre-Trained Models
This is a more engaging technique, where we do not just directly depend on the features extracted from the pre-trained models and replace the final layer but also selectively retrain some of the previous layers.
Deep neural networks are layered structures and have many tunable hyperparameters. The role of the initial layers is to capture generic features, while the later ones focus more on the explicit task at hand. It makes sense to fine-tune the higher-order feature representations in the base model to make them more relevant for the specific task. We can re-train some layers of the model while keeping some frozen in training.
Freezing vs. Fine-tuning: One logical way to increase the model's performance even further is to re-train (or "fine-tune") the weights of the top layers of the pre-trained model alongside the training of the classifier you added. This will force the weights to be updated from generic feature maps the model has learned from the source task. Fine-tuning will allow the model to apply past knowledge in the target domain and re-learn some things again.
Moreover, one should try to fine-tune a small number of top layers rather than the entire model. The first few layers learn elementary and generic features that generalize to almost all types of data. Therefore, it's wise to freeze these layers and reuse the basic knowledge derived from the past training. As we go higher up, the features are increasingly more specific to the dataset on which the model was trained. Fine-tuning aims to adapt these specialized features to work with the new dataset, rather than overwrite the generic learning.
Transfer Learning Process
Transfer learning works in practice through the following steps:
Obtain Pre-trained Model: The first step is to choose the pre-trained model that will serve as the base of the training, depending on the task. Transfer learning requires a strong correlation between the knowledge of the pre-trained source model and the target task domain for them to be compatible.
Create a Base Model: The base model is one of the architectures such as ResNet or Xception which we have selected in the first step to be in close relation to our task. We can either download the network weights which saves the time of additional training of the model. Else, we will have to use the network architecture to train our model from scratch. There can be a case where the base model will have more neurons in the final output layer than we require in our use case. In such scenarios, we need to remove the final output layer and change it accordingly.
Freeze Layers: Freezing the starting layers from the pre-trained model is essential to avoid the additional work of making the model learn the basic features. If we do not freeze the initial layers, we will lose all the learning that has already taken place. This will be no different from training the model from scratch and will be a loss of time, resources, etc.
Add New Trainable Layers: The only knowledge we are reusing from the base model is the feature extraction layers. We need to add additional layers on top of them to predict the specialized tasks of the model. These are generally the final output layers.
Train the New Layers: The pre-trained modelâs final output will most likely differ from the output we want for our model.
Improve model with fine-tuning: Not always required, fine-tuning the base model can improve model performance. This involves unfreezing portions of the base model and then training it again at a low learning rate on a new dataset.
Why Use Transfer Learning?
Transfer learning offers several benefits:
Speeds Up Training: Leveraging a pre-trained model means that you donât have to start from scratch. The model has already learned a number of features from its initial training, which can decrease the time needed to train on the new task.
Improved Performance: Models initialized with pre-trained weights often perform better than those trained from scratch, especially when data is limited.
Small Data Benefit: When you have insufficient labeled data for your task, a pre-trained model can help the model generalize better from the limited examples.
Domain Adaptation: Transfer learning can be particularly beneficial when youâre applying a model to a related domain, allowing you to reuse the knowledge that the model has already acquired.
Continuous Learning: It allows models to be updated with new data, making them more robust and adaptable as more information becomes available.
Simplifies Complex Tasks: For challenging tasks such as image recognition or natural language processing, transfer learning can simplify the development process.
When to Use Transfer Learning?
Consider using transfer learning when:
You have a limited amount of labeled data for your target task.
The source and target tasks are related enough that features learned in one can be beneficial in the other.
You have limited computational resources or want to reduce training time.
You aim to leverage state-of-the-art pre-trained models to avoid building complex models from scratch.
tags: #transfer #learning #vs #deep #learning
Popular posts:
