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Unbiased Mislabeling in Image Classification Using CNNs

Understand the effects of unbiased mislabeling on CNN models by comparing performance using clean and intentionally mislabeled MNIST image datasets. Learn to implement and evaluate mislabeling, visualize its impact on accuracy, and grasp why data quality is crucial for reliable machine learning results.

We'll cover the following...

In this lesson, we’ll learn about the impact of a small amount of unbiased mislabeling in a dataset. We aim to understand the consequences of poor-quality data by using a CNN model with two versions of the dataset—one with a clean dataset and the other with a mislabeled dataset. We’ll then compare the performance using the accuracy metric in order to gauge the impact of mislabeling.

Implementing unbiased mislabeling

To assess the impact of the dataset on the performance of a CNN model, we’ll take several steps to compare the results between a clean and mislabeled dataset.

Step 1: Importing libraries

The following code imports the libraries necessary to implement unbiased mislabeling:

Python 3.10.4
# Import necessary libraries
import keras
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
import matplotlib.pyplot as plt
from tensorflow.keras.optimizers import Adam

Step 2: Loading and creating an unbiased mislabeled dataset

The code given below loads the MNIST digit dataset using the ...