Identifying Mislabeled Images in Supervised Learning Utilizing Autoencoder

TL;DR

This paper presents a method using convolutional autoencoders and density-based clustering to identify and remove mislabeled images from training datasets, improving supervised learning accuracy.

Contribution

It introduces a novel unsupervised approach combining autoencoders and DBSCAN to detect mislabeled data in image classification tasks.

Findings

Detects over 67% of mislabeled images in experiments

Effectively visualizes mislabeled data as outliers in latent space

Improves training data quality for supervised learning

Abstract

Supervised learning is based on the assumption that the ground truth in the training data is accurate. However, this may not be guaranteed in real-world settings. Inaccurate training data will result in some unexpected predictions. In image classification, incorrect labels may cause the classification model to be inaccurate as well. In this paper, I am going to apply unsupervised techniques to the training data before training the classification network. A convolutional autoencoder is applied to encode and reconstruct images. The encoder will project the image data on to latent space. In the latent space, image features are preserved in a lower dimension. The assumption is that data samples with similar features are likely to have the same label. Noised samples can be classified in the latent space by the Density-Base Scan (DBSCAN) clustering algorithm. These incorrectly labeled data are visualized as outliers in the latent space. Therefore, the outliers identified by the DBSCAN algorithm can be classified as incorrectly labeled samples. After the outliers are detected, all the outliers are treated as mislabeled data samples and removed from the dataset. Thus the training data can be directly used in training the supervised learning network. The algorithm can detect and remove above 67\% of mislabeled data in the experimental dataset.