Transfer Learning: How to Leverage Pre-trained Models

September 20, 2025

Transfer Learning: How to Leverage Pre-trained Models

What is Transfer Learning?

Transfer Learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second task. Instead of training a model from scratch, you take an existing pre-trained model—already trained on a large dataset—and adapt it to your specific problem.

Why Use Transfer Learning?

Saves time and resources: Training deep learning models from scratch requires large datasets and lots of computing power.

Better performance: Pre-trained models have already learned useful features that can be applied to new but related tasks.

Useful with small datasets: When you have limited data, transfer learning helps avoid overfitting and improves generalization.

How Transfer Learning Works

Most transfer learning workflows involve:

Selecting a Pre-trained Model: Models trained on large, diverse datasets like ImageNet (for images) or large language corpora (for NLP) are popular choices.

Feature Extraction: Use the pre-trained model to extract features from your data without changing the model weights.

Fine-tuning: Unfreeze some of the pre-trained layers and continue training on your specific dataset to adapt the model better.

Training a New Classifier: Replace the final layers of the pre-trained model to suit your target task, then train only those layers or fine-tune the entire model.

Examples of Pre-trained Models

Domain Popular Models Dataset Trained On

Computer Vision ResNet, VGG, Inception, EfficientNet ImageNet

Natural Language Processing (NLP) BERT, GPT, RoBERTa, T5 Wikipedia, BooksCorpus

Speech Recognition Wav2Vec, DeepSpeech Large speech datasets

Step-by-Step Example: Transfer Learning in Image Classification (Using TensorFlow/Keras)

import tensorflow as tf

from tensorflow.keras import layers, models

from tensorflow.keras.applications import MobileNetV2

# Load pre-trained MobileNetV2 without the top classifier layers

base_model = MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights='imagenet')

# Freeze base model layers to prevent training initially

base_model.trainable = False

# Add new classification layers on top

model = models.Sequential([

base_model,

layers.GlobalAveragePooling2D(),

layers.Dense(128, activation='relu'),

layers.Dropout(0.5),

layers.Dense(10, activation='softmax') # Example for 10 classes

])

# Compile the model

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train on your dataset

model.fit(train_data, epochs=5, validation_data=val_data)

# Optionally unfreeze some layers for fine-tuning

base_model.trainable = True

for layer in base_model.layers[:-20]: # Freeze early layers

layer.trainable = False

# Re-compile and continue training (fine-tuning)

model.compile(optimizer=tf.keras.optimizers.Adam(1e-5), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(train_data, epochs=5, validation_data=val_data)

Tips for Successful Transfer Learning

Choose a pre-trained model close to your task. For example, use models trained on natural images for image tasks.

Freeze early layers initially. Early layers capture generic features like edges; these usually don’t need retraining.

Fine-tune later layers carefully. Gradually unfreeze layers if your dataset is large enough.

Use data augmentation to improve generalization on small datasets.

Monitor for overfitting during fine-tuning.

When NOT to Use Transfer Learning?

When your task is very different from the pre-trained model’s original domain (e.g., medical imaging vs. everyday objects).

When you have a very large dataset and computing resources, training from scratch might yield better results.

When model size or inference time is a critical constraint, as some pre-trained models can be large.

Conclusion

Transfer learning is a powerful strategy that can drastically reduce training time and improve performance on your AI projects by leveraging knowledge from pre-trained models. It’s especially useful when working with limited data or when rapid prototyping is needed.

Learn AI ML Course in Hyderabad

The Ethical Implications of AI in a Data-Driven World

How to Build a Simple Chatbot with a Pre-trained LLM

Generative AI Explained: From GANs to Diffusion Models