  State-of-the-art image and text classification models, such as Convolutional
Neural Networks and Transformers, have long been able to classify their
respective unimodal reasoning satisfactorily with accuracy close to or
exceeding human accuracy. However, images embedded with text, such as hateful
memes, are hard to classify using unimodal reasoning when difficult examples,
such as benign confounders, are incorporated into the data set. We attempt to
generate more labeled memes in addition to the Hateful Memes data set from
Facebook AI, based on the framework of a winning team from the Hateful Meme
Challenge. To increase the number of labeled memes, we explore semi-supervised
learning using pseudo-labels for newly introduced, unlabeled memes gathered
from the Memotion Dataset 7K. We find that the semi-supervised learning task on
unlabeled data required human intervention and filtering and that adding a
limited amount of new data yields no extra classification performance.
