Transfer learning is a machine learning technique where a model trained on one task is repurposed or adapted for use on a second related task. This approach has gained significant popularity due to its ability to leverage knowledge learned from one domain or task and apply it to another, typically requiring less data and computation compared to training from scratch. There are several transfer learning models, each with its own architecture and application:
Pre-trained Models: These are models that have been trained on large-scale datasets for general tasks, such as image classification, language understanding, or other domains. Examples include:
ImageNet Models: Models like VGG, ResNet, Inception, and EfficientNet pre-trained on the ImageNet dataset for image classification. BERT, GPT (Generative Pre-trained Transformer): Language models pre-trained on vast amounts of text data, enabling tasks like text generation, classification, question answering, etc. YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector): Pre-trained models for object detection in images or videos. Fine-tuning: Involves taking a pre-trained model and further training it on a smaller dataset specific to the target task or domain. By adjusting a few parameters or layers while keeping the rest of the model's weights fixed or slowly updating them, the model can adapt to the new task.
Domain-specific Transfer Learning: Some models are pre-trained on specific domains and then adapted to similar domains. For instance, a model trained on medical images might be fine-tuned for a specific medical imaging task.
Multi-task Learning: Models trained to perform multiple tasks simultaneously, with shared features across tasks. Transfer learning can be applied by leveraging the shared knowledge learned from multiple tasks to improve the performance of individual tasks.
Meta-Learning or Learning to Learn: These models are trained to learn how to adapt quickly to new tasks or domains with minimal data. Meta-learning models aim to acquire general learning skills across different tasks.
Transfer learning models have significantly contributed to advancements in various fields, including computer vision, natural language processing, recommendation systems, and more. They facilitate faster development, better performance, and reduced data requirements in solving real-world problems by leveraging the knowledge extracted from previously learned tasks or domains.
In this work, I have used the all transfer learning models for the classification of Normal and Pneumonia Chest X-ray images.