Transfer learning is an advanced technique in artificial intelligence that involves reusing a model previously trained on large datasets and adapting it to a new task, often with limited data. Unlike traditional approaches where each task requires a dedicated model and its own data, transfer learning leverages knowledge acquired from prior training. This approach optimizes resources, reduces training time, and improves performance on related tasks, especially when annotated data is scarce or expensive. Transfer learning stands out from conventional methods due to its ability to generalize and adapt quickly to new domains by reusing pre-learned representations.
Use cases and examples
Transfer learning is widely used in computer vision, natural language processing, speech recognition, and robotics. For example, a model pre-trained on ImageNet for image classification can be adapted for detecting diseases in medical scans. In text processing, models like BERT or GPT, initially trained on vast corpora, are fine-tuned for sentiment analysis or machine translation on specific datasets.
Main software tools, libraries, frameworks
Leading frameworks supporting transfer learning include TensorFlow, PyTorch, and Keras. Specialized libraries such as Hugging Face Transformers for NLP and torchvision for computer vision make it easy to access and customize pre-trained models. Other tools like FastAI or ONNX further simplify and accelerate the implementation of transfer learning in both industrial and academic contexts.
Recent developments, evolutions, and trends
Recent progress includes multi-domain transfer, federated learning, and optimizing models for better energy efficiency and reduced carbon footprint. Foundation models such as GPT-4 or CLIP are pushing the boundaries by adapting to a growing diversity of tasks with little or no examples. Research is also focusing on transfer robustness and model explainability to ensure reliability in sensitive applications.