Transfer Learning

Published 2026-02-10 in Deep Learning

Transfer Learning is a powerful deep learning technique that allows a model trained on one large dataset to be reused for a different but related task. Instead of training a neural network from scratch, transfer learning leverages pre-learned features , significantly reducing training time and improving performance—especially when labeled data is limited. The diagram illustrates how knowledge learned from a large-scale dataset such as ImageNet is transferred to a new task using a convolutional neural network (CNN). What Is Transfer Learning? Transfer learning works on the idea that early layers of deep neural networks learn general features , such as edges, textures, and shapes, which are useful across many visual tasks. These learned representations can be reused and fine-tuned for new problems. Stage 1: Pretraining on a Large Dataset At the top of the diagram, a Convolutional Neural Network (CNN) is trained on the ImageNet dataset , which contains millions of labeled images across thousands of categories. During this stage: The network learns generic visual features. Convolutional layers extract low-level and mid-level patterns. The final fully connected (FC) and softmax layers…

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