  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.
