This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec). The first approach uses both text and image features, while the second approach uses only the images. Error analysis of the two approaches shows that using only the images is more robust to the noise in the text on the memes. We utilize pre-trained DistilBERT and EfficientNet to extract features from the text and image of the memes respectively. Our classification systems obtained macro f1 score of 0.3286 for Task A and 0.5005 for Task B.

This paper presents the approach of Team KAFK for the English edition of SemEval-2020 Task 12. We use checkpoint ensembling to create ensembles of BERT-based transformers and show that it can improve the performance of classification systems. We explore attention mask dropout to mitigate for the poor constructs of social media texts. Our classifiers scored macro-f1 of 0.909, 0.551 and 0.616 for subtasks A, B and C respectively. The code is publicly released online.