Technology

Transfer learning

Repurpose pre-trained models (like ResNet or BERT) to solve new tasks with minimal data and compute.

Transfer learning bypasses the need for massive datasets by leveraging weights from models trained on massive corpuses: ImageNet for vision or Wikipedia for NLP. Instead of training 100 million parameters from scratch, you freeze the base layers and fine-tune the final classification head. This approach cuts training time by up to 90% and enables high-performance AI on niche datasets (such as identifying rare medical anomalies) where only a few hundred samples exist. Industry standards like BERT and EfficientNet rely on this logic to deliver state-of-the-art results without the massive energy costs of full-scale training.