Deep Learning Design Patterns

1. Data distributions shape model accuracy and generalization

The limitation in what you can model or learn comes down to the dataset.

Distribution types matter. Understanding data distributions is crucial for building accurate and generalizable machine learning models. The three main types - population, sampling, and subpopulation distributions - each play a unique role in shaping model performance. Population distributions represent the entire set of possible data points, while sampling distributions are subsets used to estimate population parameters. Subpopulation distributions represent specific segments within the larger population.

• Population distribution: All possible data points (e.g., all adult male shoe sizes in the US)
• Sampling distribution: Random subset used to estimate population parameters
• Subpopulation distribution: Specific segment within the population (e.g., professional athletes' shoe sizes)

Understanding these distributions helps data scientists identify potential biases, ensure representative datasets, and build models that generalize well to real-world scenarios.

2. Population, sampling, and subpopulation distributions impact machine learning

The goal with a sampling distribution is to have enough random samples of the population so that, collectively, the distributions within these samples can be used to predict the distribution within the population as a whole, and thus we can generalize a model to a population.

Balancing act of distributions. The interplay between population, sampling, and subpopulation distributions significantly influences machine learning outcomes. A well-designed sampling distribution aims to accurately represent the population, enabling models to generalize effectively. However, subpopulation distributions can introduce biases if not properly accounted for.

• Population distribution: Ideal but often unattainable target
• Sampling distribution: Practical approximation of population
• Subpopulation distribution: Potential source of bias

Key considerations:

• Ensuring random and representative sampling
• Identifying and addressing subpopulation biases
• Balancing dataset size with computational constraints

3. Out-of-distribution data challenges model performance in real-world scenarios

Let's assume you've trained a model and deployed it on a dataset, but it does not generalize to what it really sees in production as well as your evaluation data. This model is possibly seeing a different distribution of examples than what the model was trained on.

Real-world curveballs. Out-of-distribution data poses a significant challenge for deployed machine learning models. When models encounter data that differs from their training distribution, performance can degrade dramatically. This phenomenon, known as serving skew or data drift, highlights the importance of robust model design and continuous monitoring.

Causes of out-of-distribution challenges:

• Shifts in data collection methods
• Changes in real-world conditions
• Unforeseen variations in input data

Strategies to address out-of-distribution issues:

• Diverse and representative training data
• Regular model retraining and updating
• Implementing drift detection mechanisms
• Designing models with built-in robustness to distribution shifts

4. DNNs struggle with spatial relationships and out-of-distribution generalization

For the inverted dataset, it looks like our model learned the gray background and the whiteness of the digit as part of the digit recognition. Thus, when we inverted the data, the model totally failed to classify it.

DNN limitations exposed. Deep Neural Networks (DNNs) often struggle with spatial relationships and out-of-distribution generalization, as demonstrated by experiments with the MNIST dataset. When faced with inverted or shifted digits, DNNs showed poor performance, revealing their inability to capture essential features independently of background or position.

DNN challenges with out-of-distribution data:

• Inability to distinguish foreground from background
• Sensitivity to pixel-level changes in position
• Difficulty in learning spatial invariance

Attempts to improve DNN performance:

• Increasing model width (more nodes)
• Adding model depth (more layers)
• Applying regularization techniques (e.g., dropout)

These approaches showed limited success, highlighting the need for alternative architectures better suited to image recognition tasks.

5. CNNs better capture spatial relationships and improve out-of-distribution performance

Yes, it made a measurable difference. We went from a previous high of 10% accuracy on the inverted dataset to 50% accuracy. Thus, it does seem the convolutional layers help filter out (not learn) the background or whiteness of the digits.

CNN advantage revealed. Convolutional Neural Networks (CNNs) demonstrate superior performance in capturing spatial relationships and handling out-of-distribution data compared to DNNs. The convolutional layers in CNNs are better equipped to filter out irrelevant background information and learn position-invariant features of the input data.

CNN improvements over DNNs:

• Better handling of inverted images (50% vs. 10% accuracy)
• Improved performance on shifted images (57% vs. 41% accuracy)
• More efficient use of parameters (27,000 vs. 400,000+)

Key CNN advantages:

• Hierarchical feature learning
• Translation invariance
• Parameter sharing

These characteristics make CNNs more robust to certain types of out-of-distribution data, particularly in image recognition tasks.

6. Image augmentation enhances model robustness and generalization

Alternately, we are going to improve the model by using image augmentation to randomly shift the image left or right up to 20%.

Augmentation boosts performance. Image augmentation proves to be a powerful technique for improving model robustness and generalization, especially for out-of-distribution scenarios. By applying transformations such as shifts, rotations, and flips to training data, models learn to recognize objects under various conditions without increasing model complexity.

Benefits of image augmentation:

• Improved accuracy on shifted data (98% vs. 57%)
• Enhanced generalization without increased model complexity
• Expanded effective training set size

Common augmentation techniques:

• Random shifts
• Rotations
• Flips
• Scale variations
• Color jittering

Image augmentation helps models learn invariance to specific transformations, making them more resilient to variations in real-world data.

7. Combining augmentation techniques addresses multiple out-of-distribution challenges

Wow, our test accuracy on the inverted images is nearly 96%.

Synergistic augmentation effects. Combining multiple augmentation techniques can address various out-of-distribution challenges simultaneously. By incorporating both shifted and inverted images in the training data, models learn to generalize across different types of variations, significantly improving performance on diverse out-of-distribution scenarios.

Results of combined augmentation:

• Shifted images: 98% accuracy
• Inverted images: 96% accuracy

Augmentation strategy:

• Random shifts (up to 20%)
• Partial inversion of training data (10%)

This approach demonstrates the power of targeted data augmentation in addressing specific out-of-distribution challenges without increasing model complexity.

8. Real-world deployment requires understanding subpopulation biases

As a final test, I randomly selected "in the wild" images of a handwritten single digit from a Google image search. These included images that were colored, drawn with a felt-tip pen, painted with a paintbrush, and drawn in crayon by a young child. After I did my testing, I got only 40% accuracy with the CNN we just trained in this chapter.

Beware of hidden biases. Real-world deployment of machine learning models reveals the importance of understanding subpopulation biases within training data. Despite achieving high accuracy on curated test sets, models may struggle with truly "in the wild" data that differs from the training distribution in subtle ways.

Potential sources of subpopulation bias:

• Limited writing instrument variety (e.g., only pen or pencil)
• Consistent background colors or textures
• Uniform line thickness or style

Strategies for addressing subpopulation biases:

• Diverse data collection from real-world sources
• Careful analysis of model failures on edge cases
• Continuous monitoring and updating of deployed models
• Explicit testing on various subpopulations

Understanding and addressing these biases is crucial for building truly robust and generalizable machine learning models that perform well in diverse real-world scenarios.

Last updated: August 8, 2024