Transfer Learning

Transfer Learning: Using Data from Different Tasks

Transfer Learning Overview

Powerful Technique

Transfer learning allows using data from totally different tasks to improve performance on your application, especially when you don’t have much data.

How Transfer Learning Works

Example: Handwritten Digit Recognition

Problem: Recognize digits 0-9 but limited labeled training data

Solution Process:

1. Start with Large Dataset

   • Find dataset with 1 million images
   • 1,000 different classes (cats, dogs, cars, people, etc.)
   • Train neural network on this large, diverse dataset

2. Learn General Parameters

   • Network learns W¹, b¹ for first layer
   • W², b² for second layer
   • Continue through W⁴, b⁴
   • W⁵, b⁵ for output layer (1,000 units)

3. Adapt for Target Task

   • Copy first four layers (W¹,b¹ through W⁴,b⁴)
   • Replace output layer with 10 units (digits 0-9)
   • Initialize new W⁵, b⁵ from scratch

Training Options

Option 1: Train Only Output Layer

• Fix parameters: Keep W¹,b¹ through W⁴,b⁴ unchanged
• Train only: New output layer parameters W⁵,b⁵
• Best for: Very small target datasets

Option 2: Train All Parameters

• Initialize: Use pre-trained values for W¹,b¹ through W⁴,b⁴
• Fine-tune: Update all parameters including output layer
• Best for: Larger target datasets

Small Dataset

Recommendation: Option 1

• Only update output layer
• Preserve learned features
• Prevent overfitting

Larger Dataset

Recommendation: Option 2

• Fine-tune all parameters
• Adapt features to new task
• Better performance potential

Transfer Learning Process

Step 1: Supervised Pre-training

• Train neural network on large dataset (1 million images)
• Learn general features from diverse tasks
• Often done by researchers and shared publicly

Step 2: Fine-tuning

• Adapt pre-trained parameters to specific task
• Run gradient descent on target dataset
• Adjust weights for handwritten digit recognition

Note

Community Benefit: Often you don’t need to do pre-training yourself. Many researchers share pre-trained models online for free download and use.

Why Transfer Learning Works

Hierarchical Feature Learning

Early layers learn low-level features:

• Layer 1: Edge detection (lines, curves)
• Layer 2: Corner and basic shape detection
• Layer 3: More complex shapes and curves

These features generalize across tasks:

• Edge detection useful for many vision problems
• Basic shapes relevant to digits, letters, objects
• Transfer from cats/dogs/cars helps with digit recognition

Intuition Behind Success

Training on diverse objects (cats, dogs, cars, people) teaches networks to detect:

• Edges and corners: Fundamental visual elements
• Basic geometric shapes: Building blocks of complex objects
• Generic visual features: Applicable across many computer vision tasks

Implementation Requirements

Input Type Consistency

Critical requirement: Same input type for pre-training and fine-tuning

Computer Vision

• Pre-training: Images of specified dimensions
• Fine-tuning: Images of same dimensions
• ✅ Compatible for transfer

Speech Recognition

• Pre-training: Audio data
• Fine-tuning: Audio data
• ❌ Vision pre-training won’t help audio tasks

Text Processing

• Pre-training: Text data
• Fine-tuning: Text data
• ❌ Image pre-training won’t help text tasks

Practical Implementation

Two-Step Process

Step 1: Obtain Pre-trained Model

• Download neural network pre-trained on large dataset
• Ensure same input type as your application
• Alternative: Train your own (but downloading usually better)

Step 2: Fine-tune on Your Data

• Replace output layer for your specific task
• Apply Option 1 or Option 2 training approach
• Use much smaller dataset (sometimes even 50 images)

Success with Small Datasets

Remarkable Results

“I’ve sometimes trained neural networks on as few as 50 images that worked quite well using this technique, when pre-trained on much larger datasets.”

Advanced Examples

Modern Applications

• GPT-3: Pre-trained on large text datasets, fine-tuned for specific applications
• BERT: Pre-trained language model for various NLP tasks
• ImageNet: Pre-trained vision models for computer vision applications

Community Collaboration

Transfer learning exemplifies machine learning community collaboration:

• Researchers share pre-trained models freely
• Build on each other’s work collectively
• Achieve better results than individual efforts
• Open sharing of ideas, code, and parameters

This collaborative approach enables anyone to leverage powerful pre-trained models and fine-tune them for specific applications, dramatically reducing the data requirements for achieving good performance.