Transfer Learning for Computer Vision: Practical Techniques for CNNs and Vision Transformers

Author: Debabrata Pruseth
Publication Date: 2025/10/05
Document Type: Technical Note / Research Article
Language: English

Abstract

Transfer learning has become a central technique in modern computer vision because it enables high-performing visual recognition systems to be developed with limited labelled data, reduced compute cost, and shorter experimentation cycles. Earlier transfer learning workflows were dominated by convolutional neural networks such as ResNet and EfficientNet, where pretrained feature extractors were adapted to downstream classification, detection, or segmentation tasks. More recently, Vision Transformers, multimodal vision-language models, and foundation segmentation models have expanded the transfer learning landscape by introducing patch-based representation learning, self-attention, prompt-based adaptation, and parameter-efficient fine-tuning. This paper presents a practical research-oriented overview of transfer learning for computer vision, with emphasis on model selection, adaptation strategies, fine-tuning recipes, self-supervised pretraining, and troubleshooting. It compares feature extraction, partial fine-tuning, full fine-tuning, adapter-based learning, LoRA-style parameter-efficient adaptation, masked autoencoding, contrastive learning, and prompt-based transfer. The objective is to provide a structured decision framework for practitioners who need to adapt CNNs, Vision Transformers, CLIP-like models, or segmentation foundation models to real-world domains such as medical imaging, agriculture, satellite analysis, retail, and industrial inspection.

Keywords
transfer learning, computer vision, convolutional neural networks, Vision Transformer, ViT, LoRA, adapters, CLIP, SAM, MAE, self-supervised learning, fine-tuning

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Transfer Learning for Computer Vision: Practical Techniques for CNNs and Vision Transformers

Suggested Citation
Pruseth, D. (2026). Transfer Learning for Computer Vision: Practical Techniques for CNNs and Vision Transformers. Debabrata Pruseth AI blog.

Companion Note
This page provides the abstract and full-text PDF for the research version of the article. A companion blog post explains the same work in a more narrative and implementation-focused style.

Read the companion blog:
https://debabratapruseth.com/transfer-learning-for-vision/