Transfer Learning vs Fine Tuning: Key Differences Explained

When building machine learning models, especially deep learning architectures, training from scratch can be resource-intensive and time-consuming. This is where transfer learning and fine-tuning come in.

Both methods leverage pre-trained models to save computation time and improve accuracy, but they differ in how much of the model is reused and retrained.

Let’s explore the differences between Transfer Learning and Fine Tuning, their applications, and provide some Python code examples.

What Is Transfer Learning?

Transfer learning is the process of utilizing a pre-trained model (typically trained on a large dataset, such as ImageNet) for a different, yet related task.

Instead of starting from scratch, we reuse the learned features (like edges, textures, or shapes in image models) and only replace the final classifier layer.

👉 Example: Using a model trained on millions of images to classify medical X-rays.

Python Example: Transfer Learning with Keras (Feature Extraction)

from tensorflow.keras.applications import VGG16
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras import Sequential

# Load pre-trained VGG16 without the top classifier
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224,224,3))

# Freeze base model layers
for layer in base_model.layers:
    layer.trainable = False

# Add custom classifier
model = Sequential([
    base_model,
    Flatten(),
    Dense(128, activation='relu'),
    Dense(2, activation='softmax')  # Binary classification
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
print("Transfer learning model ready!")

In this case, the base model is frozen and only the classifier is trained.

What Is Fine Tuning?

Fine-tuning takes transfer learning a step further. Instead of freezing the entire pre-trained model, we unfreeze some of the deeper layers and retrain them along with the classifier.

This enables the model to adapt its feature representations to the new dataset more closely.

👉 Example: Fine-tuning ResNet for satellite image classification.

Python Example: Fine Tuning with Keras

from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D

# Load ResNet with pretrained weights
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3))

# Unfreeze last few layers for fine tuning
for layer in base_model.layers[-10:]:
    layer.trainable = True

# Add custom classifier
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(128, activation='relu')(x)
preds = Dense(3, activation='softmax')(x)  # Example: 3-class problem

model = Model(inputs=base_model.input, outputs=preds)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
print("Fine tuning model ready!")

Here, the model learns new patterns while still leveraging pre-trained knowledge.

Transfer Learning vs Fine Tuning: Key Differences