Advanced Applied Deep Learning
Lecture Course
Sheng Yun Wu
Lecture Course
Sheng Yun Wu
Objective:
To introduce students to regularization techniques used to prevent overfitting in deep learning models, especially Convolutional Neural Networks (CNNs). By the end of the week, students will understand how to apply techniques like dropout, data augmentation, and batch normalization to improve model generalization and prevent overfitting.
Lecture 1: Understanding Overfitting and Regularization
4.1 What is Overfitting?
Definition of Overfitting:
Overfitting occurs when a model performs well on the training data but fails to generalize to unseen data.
Causes of overfitting: Too many parameters, insufficient data, and a model that is too complex for the dataset.
Symptoms of Overfitting:
High accuracy on the training set but low accuracy on the validation or test set.
Large gap between training loss and validation loss during training.
4.2 Regularization Techniques Overview:
Regularization is a set of techniques used to reduce overfitting by penalizing complex models and encouraging simpler models that generalize better.
Types of Regularization:
L1 Regularization (Lasso): Adds a penalty equal to the absolute value of the magnitude of the coefficients.
L2 Regularization (Ridge): Adds a penalty equal to the square of the magnitude of the coefficients.
Elastic Net: A combination of L1 and L2 regularization.
Why Regularization Helps:
It prevents the model from fitting to noise in the training data, allowing it to generalize better to unseen data.
Lecture 2: Practical Regularization Techniques in CNNs
4.3 Dropout:
What is Dropout?
Dropout is a technique where, during training, a random subset of neurons is “dropped out” or turned off in each forward pass.
How Dropout Works:
During each training iteration, a percentage of neurons are randomly deactivated.
Prevents co-adaptation of neurons and forces the network to learn more robust features.
Where to Apply Dropout:
Dropout is typically applied after fully connected layers but can also be applied after convolutional layers in some architectures.
Impact on Model Performance:
Reduces overfitting by making the network more robust to noisy inputs and encourages redundancy in feature learning.
4.4 Data Augmentation:
What is Data Augmentation?
Data augmentation involves artificially increasing the size of the training dataset by applying transformations such as rotations, flips, cropping, zooming, and translations to the input images.
Common Data Augmentation Techniques:
Rotation, horizontal/vertical flipping, random cropping, zooming, color shifts, brightness adjustments.
Benefits:
Reduces overfitting by exposing the model to a wider variety of inputs, making it more generalizable.
Especially useful when working with small datasets.
4.5 Batch Normalization:
What is Batch Normalization?
Batch normalization standardizes the inputs to a layer by normalizing the activations of the neurons for each mini-batch.
How Batch Normalization Works:
It normalizes each mini-batch to have zero mean and unit variance, which helps stabilize training and allows for higher learning rates.
Benefits:
Improves convergence speed, reduces internal covariate shift, and helps regularize the model by reducing overfitting.
Where to Apply Batch Normalization:
Typically applied after the activation function of each layer in a deep network.
Practical Session: Applying Regularization in CNNs
Objective: Implement regularization techniques in a CNN model to improve its generalization and reduce overfitting.
Dataset: CIFAR-10 or MNIST dataset.
Key Steps:
Step 1: Baseline Model Without Regularization
Build a simple CNN without any regularization techniques.
Train the model on the CIFAR-10 dataset and evaluate its performance on both the training and validation sets.
Observe signs of overfitting (i.e., training accuracy significantly higher than validation accuracy).
Step 2: Applying Dropout
Apply dropout after fully connected layers (e.g., 50% dropout rate).
Train the model again and compare the performance.
Observe if the gap between training and validation accuracy is reduced.
Step 3: Applying Data Augmentation
Implement data augmentation techniques using Keras or TensorFlow’s data preprocessing tools.
Apply random rotations, flips, and shifts to the training dataset.
Train the model with data augmentation and observe the impact on overfitting.
Step 4: Applying Batch Normalization
Add batch normalization layers to the CNN after the activation functions in the convolutional layers.
Train the model again and observe the effect on training stability and performance.
Assignment for Week 4:
Coding Assignment:
Experiment with the CNN model trained in class and apply the following:
Dropout at different layers with varying rates (e.g., 20%, 30%, 50%).
Data augmentation techniques like random zoom, random cropping, and brightness shifts.
Batch normalization in different positions in the network.
Analysis:
Compare the performance of the CNN model with and without regularization techniques.
Evaluate how regularization affects overfitting and generalization on the test set.
Reading Assignment:
Read Chapter 5 of "Advanced Applied Deep Learning" by Umberto Michelucci.
Focus on understanding the impact of regularization techniques on CNNs and when to use each method.
Summary of Key Concepts:
Overfitting and its causes in deep learning models.
Dropout: Randomly deactivating neurons during training to reduce overfitting.
Data Augmentation: Artificially increasing the dataset by applying transformations to the input images.
Batch Normalization: Normalizing the activations in each mini-batch to improve training stability and reduce overfitting.
Practical experience applying these regularization techniques to CNN models for improved generalization.
This week provides essential tools for reducing overfitting in CNN models and improving their ability to generalize to unseen data. Students will learn how to implement and tune various regularization techniques that are widely used in deep learning.